Episodit
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Show Notes(01:41) Salma reflected on her upbringing in Paris (France) and her love for math since a young age.(04:05) Salma talked about her decision to dive deeper into statistics.(07:39) Salma recalled her 5 years in investment banking in Hong Kong.(10:44) Salma doubled down on the cultivation of her resilience during this period.(13:45) Salma shared the founding story Sifflet.(15:53) Salma touched on her co-founder dynamics with Wajdi Fathallah and Wissem Fathallah.(18:50) Salma explained the concept of Full Data Stack Observability to the uninitiated.(22:14) Salma extrapolated on Sifflet's approach to data observability.(24:37) Salma discussed Sifflet's data quality focus with automated monitoring coverage and over 50 data quality templates.(27:28) Salma discussed Sifflet's data lineage solution with field-level lineage, root cause analysis, and incident management/business impact assessment.(31:47) Salma discussed Sifflet's data catalog with a powerful metadata search engine and centralized documentation for all data assets.(33:46) Salma expanded on the full-stack mindset as a data vendor.(37:03) Salma emphasized the importance of integrations with other data tools.(38:52) Salma touched on product features such as Flow Stopper to stop vulnerable pipelines from running at the orchestration layer and Metrics Observability to extend the observability framework to the semantic layer.(43:40) Salma unpacked her article on building a modern data team.(47:54) Salma shared some hiring lessons to attract the right people to Sifflet.(54:02) Salma emphasized the importance of company branding.(55:01) Salma shared her thoughts on building a startup culture.(57:26) Salma briefly mentioned her process of working with design partners in the early stage.(01:00:17) Salma emphasized her enthusiasm for the broader data community.(01:02:05) Conclusion.Salma's Contact InfoLinkedInTwitterMediumSifflet's ResourcesWebsite | LinkedIn | Twitter | DocsData Catalog | Data Quality Monitoring | Data Lineage | IntegrationsMentioned ContentPeopleZhamak Dehghani (Creator of Data Mesh and Founder of NextData)Benoit Dageville, Thierry Cruanes, and Marcin Zukowski (Founders of Snowflake)Books"The Hard Thing About Hard Things" (by Ben Horowitz)"The Boys In The Boat" (by Daniel James Brown)Notes
My conversation with Salma was recorded back in late 2022. Since then, I recommend checking out the launch of Sifflet AI Assistant and this blog post on 2024 data trends.
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Show Notes(01:53) Suresh went over his college experience studying Electronics Engineering at the National Institute of Technology Karnataka.(04:35) Suresh recalled his 9-year engineering career at Sylantro Systems.(08:47) Suresh talked about the origin of Apache Hadoop at Yahoo.(11:05) Suresh dissected the high-level design architecture of the Hadoop Distributed File System (HDFS).(15:36) Suresh reflected on his decision to become a co-founder of Hortonworks, which focused on bringing Hadoop training and support to enterprise customers.(17:36) Suresh unpacked the evolution of the Hortonworks Data Platform - which includes Hadoop technology such as HDFS, MapReduce, Pig, Hive, HBase, ZooKeeper, and additional components.(20:30) Suresh shared his lessons from developing and supporting open-source software designed to manage big data processing.(23:43) Suresh walked through the evolution of Uberâs Data Platform.(28:03) Suresh described Uber's journey toward better data culture from first principles.(34:00) Suresh explained his motivation to start the OpenMetadata Project.(37:21) Suresh elaborated on OpenMetadata's five design principles: schema-first, extensibility, API-centric, vendor-neural, and open-source.(40:17) Suresh highlighted OpenMetadata's built-in features to power multiple applications, such as data collaboration, metadata versioning, and data lineage.(44:38) Suresh emphasized his priority for the open-source roadmap to adapt to the community's needs.(47:05) Suresh explained the architecture of OpenMetadata - which goes deep into the push-based and pull-based characteristics of metadata ingestion and consumption.(51:47) Suresh shared the long-term vision of his new company Collate, which powers the OpenMetadata initiative.(53:36) Suresh shared valuable hiring lessons as a startup founder.(56:30) Suresh shared fundraising advice to founders who want to seek the right investors for their startups.(57:50) Closing segment.Suresh's Contact InfoLinkedInTwitterGitHubOpenMetadata's ResourcesWebsite | TwitterSlack | GitHub | CommunityDocumentationCollateMentioned ContentPeopleJoe Littlejohn (jsonschema2pojo)Sriharsha ChintalapaniBookThe Innovator's Dilemma (by Clayton Christensen)
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Puuttuva jakso?
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Show Notes(02:20) Krishna described his academic experience getting an MS in Computer Science from the University of Minnesota - where he developed efficient tools for text document clustering and pattern discovery.(05:36) Krishna recalled his 5.5 years at Microsoft working on Bing's search engine.(08:32) Krishna talked about the challenges of competing against Google Search.(10:22) Krishna shared the high-level technical and operational challenges encountered during the development and scaling phase of Twitter Search.(14:55) Krishna revealed vital lessons from building critical data infrastructure at Twitter.(17:54) Krishna touched on his time at Pinterest as the head of data engineering - leading a team working on all things data from analytics, experimentation, logging, and infrastructure.(20:05) Krishna reviewed the design and implementation of real-time analytics, ETL-as-a-Service, and an A/B testing platform at Pinterest.(24:40) Krishna unpacked the major ML model performance issues while running Facebook's feed ranking platform.(28:18) Krishna distilled lessons learned about algorithmic governance from Facebook.(31:38) Krishna provided leadership lessons from building teams that create scalable platforms and delightful consumer products on Twitter, Pinterest, and Facebook.(33:19) Krishna shared the founding story of Fiddler AI, whose mission is to build trust into AI.(37:56) Krishna unpacked the key challenges and tools in his 2019 article "AI needs a new developer stack."(40:49) Krishna discussed the evolution of MLOps over the past 4 years.(42:48) Krishna explained the benefits of using the Model Performance Management (MPM) framework to address enterprise MLOps challenges.(47:01) Krishna gave a brief overview of capabilities within Fiddler's MPM platform, such as model monitoring, explainable AI, analytics, and fairness.(50:28) Krishna highlighted research efforts inside Fiddler concerning explainability, drift metric calculation, and fairness.(53:17) Krishna discussed the challenges with monitoring for NLP and Computer Vision models.(57:18) Krishna zoomed in on Fiddler's approach to model governance for the modern enterprise.(01:02:24) Krishna distilled valuable lessons learned to attract the right people who are excited about Fiddler's mission and aligned with Fiddler's culture.(01:06:08) Krishna reflected on the evolution of Fiddler's company culture.(01:09:19) Krishna shared the challenges of finding the early design partners and defining a new category of Responsible AI.(01:12:23) Krishna gave fundraising advice to founders who are seeking the right investors for their startups.(01:14:45) Closing segment.Krishna's Contact InfoLinkedInTwitterMediumFiddler's ResourcesWebsite | LinkedIn | Twitter | YouTubeAbout | Customers | CareersAI Observability | Model Monitoring | Explainable AI | Fairness | AnalyticsBlog | Docs | ResourcesMentioned ContentPeopleGoku Mohamandas (Made With ML and Anyscale)Krishnaram Kenthapadi (Chief AI Officer & Chief Scientist at Fiddler)Books"The Hard Thing About Hard Things" (Ben Horowitz)"The Five Dysfunctions of A Team" (Patrick Lencioni)Notes
My conversation with Krishna was recorded more than a year ago. Since then, I'd recommend checking out these Fiddler's resources:
Strategic investments in Fiddler by Alteryx Ventures, Mozilla Ventures, Dentsu Ventures, and Scale Asia Ventures.Fiddler introduces an end-to-end workflow for robust Generative AI back in May 2023.Krishna's thought leadership on LLMOps and the missing link in Generative AI.About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Show Notes(01:59) Emanuel reflected on his upbringing in Switzerland and his 4-year apprenticeship in Software Engineering and Business at Credit Suisse.(06:09) Emanuel recalled his 4-year program in Computer Science at HSR (University of Applied Sciences Rapperswil).(08:43) Emanuel touched on his decision to pursue a Masterâs degree at Brown University in the US.(11:58) Emanuel explained his decision to continue with a Ph.D. degree at Brown under the advisement of Professor Andy van Dam and summarized the arc of his Ph.D. research focus.(14:50) Emanuel highlighted his first research paper called PanoramicData on Interactive Data Exploration.(16:42) Emanuel shared his thoughts on common traits of a successful researcher.(18:03) Emanuel emphasized the focus of his research at the intersection of Human-Computer Interaction, Information Visualization, and Data Analysis.(20:38) Emanuel shared valuable lessons from interning twice at Microsoft Research in Redmond.(22:34) Emanuel talked about his time as a postdoc in Professor Tim Kraskaâs group at the CSAIL at MIT.(24:59) Emanuel shared the founding story of Einblick - a visual computing platform that enables data teams to answer tougher, more meaningful questions by making advanced analytics and model building more streamlined and accessible.(29:06) Emanuel touched on the responsibilities of Einblick's 5 co-founders.(30:23) Emanuel highlighted technical challenges of building Einblick's integrated environment for descriptive, predictive, and prescriptive analytics.(32:09) Emanuel mentioned the collaboration challenge in data and brought up Einblick's real-time remote collaboration through video-enabled data whiteboards.(35:39) Emanuel highlighted the challenges of working with computational notebooks and brought up the benefits of using Einblick's collaborative visual canvas.(38:54) Emanuel unpacked the challenges of commercializing an academic research project.(40:27) Emanuel gave a broad overview of Einblick's go-to-market strategy.(43:55) Emanuel shared valuable hiring lessons to attract the right people who are aligned with Einblickâs cultural values.(47:05) Emanuel shared fundraising advice to founders who are seeking the right investors for their startups.(49:20) Emanuel shared the similarities and differences between being a researcher and being a founder.(50:43) Closing segment.Emanuel's Contact InfoWebsiteGoogle ScholarLinkedInTwitterEinblick's ResourcesWebsite | Twitter | LinkedInDocs | BlogChartGen AINotebook Feature Release (2022)Video-Based Collaboration Release (2021)Mentioned ContentPapers and ProjectsPanoramicData is a hybrid pen and touch system for visual data exploration (Infovis 2014 Paper | Video)(s|qu)eries (pronounced âSqueriesâ) is a visual query interface for creating queries on sequences (series) of data based on regular expressions (CHI 2015 Paper | Summary Video)Vizdom is an interactive visual analytics system that scales to large datasets through progressive computation (VLDB Demo 2015 Paper | Health Video | Election Video)Tableur is a spreadsheet-like pen- and touch-based system that revolves around handwriting recognition - all data is represented as digital ink (CHI 2016 LBW Paper | Video)Towards Accessible Data Analysis (Emanuel's Ph.D. Dissertation at Brown, 2018)Northstar is an interactive data science platform that combines data exploration with automated machine learning (SIGMOD DEEM Paper | Video)PeopleWes McKinneyFei-Fei LiBooks"The Book of Why" (by Judea Pearl)"The Signal and The Noise" (by Nate Silver)Notes
My conversation with Emanuel was recorded back in late 2022. Since then, I recommend checking out the launch of Einblick Prompt and ChartGenAI.
About the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Timestamps(01:41) Diana shared her upbringing in Orlando and her undergraduate experience studying Economics at MIT.(03:27) Diana reflected on valuable lessons from her internships during college.(05:58) Diana brought up her learning during the year working as an investment banking analyst in the technology group at Morgan Stanley.(09:10) Diana recalled her transition from investment banking into venture capital at Norwest Venture Partners.(11:52) Diana discussed the takeaways from her time as a venture associate meeting entrepreneurs on a regular cadence.(14:23) Diana recalled her decision to leave venture capital and become the first hire at the product organization at Cockroach Labs.(19:00) Diana went over the challenges and learning curves as a non-technical first product hire at Cockroach.(21:26) Diana extrapolated on the idea of determining the best-fit product strategy rather than blindly following frameworks.(23:55) Diana described her experience as the first hire into the product organization at TimescaleDB.(26:40) Diana highlighted the challenges of open-source GTM.(27:56) Diana reflected on her 3-part blog series on building a product for the most dissatisfied customers first, the majority next, and the full need in the long run.(30:40) Diana shared 2 tactical lessons to cultivate focus as a product manager.(32:44) Diana share the founding story of Correlated alongside her co-founders, Tim Geisenheimer and John Pena.(35:38) Diana briefly touched on her 2 entrepreneurial attempts during COVID-19.(38:30) Diana unpacked the notion of Product-Led Revenue and described how Correlated works at a high level.(40:49) Diana highlighted the role of integrations within Correlated's product strategy.(43:04) Diana mentioned Correlated's product-led playbooks to help users manage their product-led strategy from start to finish.(45:40) Diana explained how she leveraged customer feedback to ship the feature called PQL Scoring that leverages machine learning to identify the best leads.(48:51) Diana shared the consistent principles that have remained the same for successful communication in Product Management.(52:30) Diana discussed her learnings on customer discovery at early-stage startups.(56:08) Diana reflected on the early signs of product-market fit that carry through all of her startups.(58:58) ConclusionDiana's Contact InfoLinkedInTwitterMediumSubstackCorrelated's ResourcesWebsite | LinkedIn | TwitterProduct Overview | How Correlated WorksBlog | Podcast | DocsPLG Playbook LibraryCorrelated Launches to Bring Product-Led Revenue to Market with $8.3M in FundingWhat Is Product-Led Revenue?Correlated launches PQL Scoring to accelerate your product-led strategyMentioned ContentBlog Posts"The Standard Due Diligence Process" (Jan 2016)"Mistakes to Avoid when Pitching to a VC" (Jan 2016)"My Startup Litmus Test" (Feb 2016)"Why I left VC to join Cockroach Labs" (April 2017)"My First 90 Days as the First Product Hire" (May 2017)"Coding != Technical: What It Means to be Technical as a PM" (Aug 2017)"How learning to sell makes for a better product manager" (Nov 2017)"Roadmap Planning: Users First, Features Second" (March 2018)"Build something people will use more than once" (May 2019)"Focus on the unhappiest, most dissatisfied customers first" (May 2019)"Build for the majority" (May 2019)"Why user interviews can fail you when starting a startup" (Sep 2021)"Tackling the challenges of communicating effectively in product management" (Jan 2022)"Some Learnings on Customer Discovery at Early-Stage Startups" (May 2022)"4 early signs of product-market fit" (Sep 2022)"Give customers what they want, but not what they ask for" (Sep 2022)PeopleLenny RachitskyJulie ZhuoNate StewartJeff SposettiBook"Crossing The Chasm" (by Geoffrey Moore)About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Timestamps(01:45) Jason shared the formative experiences of his upbringing in the Bay Area and coming of age in the âMoneyballâ era of baseball.(05:03) Jason described his overall academic experience at Stanford - where he studied Mathematical and Computational Science with a minor in Classical Studies.(09:15) Jason reflected on his experience participating in the Mayfield Fellowship at Stanford.(12:03) Jason recalled his time being a part of the business operations team during a high-growth period at Opendoor.(14:25) Jason talked about lessons learned working as a management consultant at McKinseyâs Bay Area practice.(15:59) Jason reminisced about his time at the AI Fund startup studio - where he launched AI-enabled SaaS startups by iterating on prototypes, signing design partners, and recruiting the founding team.(19:25) Jason explained his decision to join the investment team at Greylock Partners.(22:24) Jason walked through his journey proving value as a new investor.(24:41) Jason unpacked his checklist for evaluating early-stage enterprise investment opportunities.(27:09) Jason explained his seed investment in Onehouse - a cloud-native managed lakehouse service that makes data lakes easier, faster, and cheaper.(30:31 ) Jason explained his Series A investment in Baseten - which builds a powerful software toolkit that empowers technical data science teams to serve, integrate, design, and ship their custom ML models efficiently.(33:23) Jason touched on advice for his portfolio companies in hiring decisions and navigating product/GTM strategy.(37:00) Jason unpacked key takeaways from Greylockâs Castles in the Cloud project.(39:58) Jason dissected key trends in the markets of security, AI/ML, management and governance, and edge computing (as shown in "VC Funding for the Cloud").(46:24) Jason elaborated on his vision of "The Next Cloud Data Platform" - which examines how the data warehouse, lakehouse, and semantic layer could combine to create a platform for data applications.(50:55) Jason shared a few books that have greatly influenced his life.(52:22) Closing segment.Jason's Contact InfoLinkedInTwitterGreylockMentioned ContentBooks"Moneyball" (by Michael Lewis)"Why The West Rules For Now" (by Ian Morris)"Snow Crash" (by Neil Stephenson)"Cryptonomicon" (by Neil Stephenson)"Termination Shock" (by Neil Stephenson)"Principles for Dealing with the Changing World Order" (by Ray Dalio)PeopleDavid Luan (Founder and CEO of Adept)Alex Ratner (Co-Founder and CEO of Snorkel AI)Frank Slootman (CEO of Snowflake)Clement Delangue (Co-Founder and CEO of HuggingFace)Notes
My conversation with Jason was recorded back in late 2022. Since then, I recommend checking out these resources:
His blog post on the next platform opportunity in cybersecurityGreylock's investment in LlamaIndexAbout the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Show Notes(01:44) Heather talked about her upbringing, her education, and her 14-year career at Liberty Mutual Insurance.(06:50) Heather emphasized the benefits of her education in organizational design and management.(08:56) Heather walked through her decision to shift from underwriting to technology and data at Liberty.(13:55) Heather commented on her 5 years as a product manager at Crum & Forster.(20:14) Heather described her 2-year experience as the Global Head of Technology at Innovisk (a Wills Towers Watson ).(23:54) Heather distinguished the work environments in a startup and a large company.(26:17) Heather recalled different data strategy initiatives she led at Brown and Brown Insurance.(28:33) Heather explained issues in the insurance value chain and the role of data to help tackle them.(34:17) Heather discussed her role as the founding Chief Data Officer at Accelerant Holdings.(40:18) Heather brought up the data quality issues that Accelerant risk exchange helped solve.(45:52) Heather gave advice to organizations to move from data governance to Data Intelligence.(48:45) Heather provided her perspective on hiring data talent.(50:40) Heather looked at the insurance transformation from a technology perspective.(52:52) Heather talked about engaging women in technical fields.(54:22) Closing segment.Heather's Contact InfoLinkedInAccelerant | AboutRelevant ReadingBloomberg | Boehly'sHeather's Eldridge Bets on Accelerant at $2.2 Billion ValuationInsurance Business Mag | Accelerant: An insurtech that defies categoriesBusiness Insurance | Accelerant establishes $175 million sidecar reinsurerAI Times Journal | Data Intelligence is Key to Understanding our Customers â Chief Data Officer, Accelerant HoldingsLightco | Insurance Innovators Top 100Business Wire | Accelerant Launches the Accelerant Risk Exchange to Reimagine InsuranceMentioned ResourcesPeopleZhamak DehganiCassie KozyrkovAllie MillerBookData Mesh: Delivering Data-Driven Value at Scale (by Zhamak Dehghani)About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Show Notes(01:36) Bob shared formative experiences of his upbringing with exposure to technology.(05:08) Bob discussed his time building software for fun as a teenager.(07:08) Bob reflected on his education in music and his decision to transition to a career in software development.(10:52) Bob explained his project control(human, data, sound) while working on his consultancy agency Kubrickology.(14:25) Bob discussed how using software can enhance our creativity.(17:28) Bob talked about his fascination with merging physical and digital realms.(19:41) Bob recalled his TEDx talk that introduced three high-level ideas about why software works well based on its ability to adapt to our language.(24:57) Bob shared the founding story of Weaviate.(29:40) Bob talked about his process of choosing his co-founders.(31:29) Bob unpacked his high-level thinking around creating a business model around the open-source project.(38:06) Bob defined a vector search engine for the uninitiated.(40:49) Bob gave a brief overview of the high-level design of Weaviate.(43:15) Bob talked about Weaviate's production-ready features, such as horizontal scalability and graph-like connections between objects.(45:45) Bob reviewed the use cases for Weaviate that he is most proud of.(49:31) Bob emphasized the importance of engaging open-source contributors to generate valuable product feedback.(55:03) Bob talked about the pricing model for Weaviate Cloud Service.(57:59) Bob anticipated the evolution of the tooling landscape within the AI-first database ecosystem to support the increasing adoption of unstructured data.(01:02:31) Bob shared valuable hiring lessons to attract the right people to join Weaviate.(01:04:36) Bob explained his process of identifying people who align with the cultural values of Weaviate.(01:08:27) Bob gave fundraising advice to founders who are seeking the right investors for their startups.(01:12:17) Bob highlighted his thinking around being a remote-first company and building an open-source brand.(01:16:28) Closing segment.Bob's Contact InfoWikipediaLinkedInTwitterGitHubWTF Medium BlogYouTubeWeaviate's ResourcesWebsite | Twitter | Slack | Forum | GitHubBlog | Podcast | PlaybookMentioned ContentPeopleSam Ramji (DataStax)Paul Graham (Y Combinator)Book"Hackers and Painters" (by Paul Graham)Notes
My conversation with Bob was recorded back in late 2022. Since then, I recommend checking out these resources:
SeMI Tech becomes WeaviateThe $50M Series B funding led by Index with participation from BatteryThe public beta of Weaviate Cloud ServiceBob's posts on Weaviate's organic growth and 4th birthdayAbout the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Show Notes(01:40) Sakib shared formative experiences of his upbringing in SoCal and his undergraduate experience at the University of Pennsylvania.(05:24) Sakib recalled his favorite classes at Penn.(07:55) Sakib reflected on his internship experience at Innova Dynamics and Morgan Stanley.(11:04) Sakib reflected on his decision to pursue a career in venture capital at Bessemer Venture Partners.(14:02) Sakib walked through his process of proving value as a new investor.(16:21) Sakib explained his process of forming clear investment theses.(18:35) Sakib talked about his brief year as a product manager at Viagogo before going back to Bessemer.(22:04) Sakib dissected his investments in LaunchDarkly and PagerDuty (in the domain of developer-centric platforms).(24:16) Sakib explained his investments in Coiled, Prefect, and Arcion Labs (in the domain of data infrastructure).(25:59) Sakib walked through his investment in Guild Education and Tribe (in the domain of education and community management).(29:06) Sakib shared advice to portfolio companies in terms of navigating hard decisions and growth strategy.(34:18) Sakib outlined Bessemer's roadmap on data infrastructure - which looks at the wave of startups enabling the next generation of data-driven businesses.(39:57) Sakib brought up the products that help abstract away complexity from data engineering problems.(41:34) Sakib highlighted the tools that power the next generation of data scientists.(43:27) Sakib emphasized the emergence and evolution of metadata management(46:48) Sakib unpacked the evolution of ML infrastructure.(49:28) Sakib examined the key trends and opportunities that will define the next wave of BI and data analytics software.(52:56) Sakib shared his investment perspectives on climate change and student builders.(55:29) Closing segment.Sakib's Contact InfoProfile PageLinkedInTwitterMentioned ContentPeopleSarah Catanzaro (General Partner of Amplify Partners)Ed Sim (Founder of Boldstart Ventures)Mike Speiser (Managing Partner of Sutter Hill Ventures)Book"The Idea Factory" (by Jon Gertner)Notes
My conversation with Sakib was recorded back in late 2022. Since then, I recommend checking out these resources:
This blog post on the era of intelligent searchBessermer's AI Roadmap and the ChatBVP botBessemer's 2023 Cloud 100 Benchmarks ReportAbout the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Show Notes(01:56) Casber reflected on his experience growing up in China and moving to the US to pursue an undergraduate degree in business at UC Berkeley.(04:45) Casber recalled working on his startup Etch.ai and interning at Wish during his time at Berkeley.(10:03) Casber differentiated investing patterns for B2B and B2C startups.(12:10) Casber reflected on his investment banking experience at Bank of America Merrill Lynch and transitioning to venture capital at Sapphire Ventures.(17:06) Casber gave some advice for analysts who want to transition into the tech and venture industry.(20:06) Casber provided a high-level overview of Sapphire Ventures and its investment focus.(21:54) Casber recalled his early days as a new investor and his process of adding value to portfolio companies.(24:58) Casber dissected his investments in the Series F round of JumpCloud and the Series B round of Uptycs (in the domain of security).(33:03) Casber explained his investments in the Series B round of Tetrate and the Series A of Zesty (in the domain of enterprise infrastructure).(35:59) Casber walked through his investment in the Series D round of Dremio (in the domain of data and analytics).(39:09) Casber shared his advice to his portfolio companies in terms of navigating hiring decisions and growth strategy.(43:06) Casber unpacked the three strategies software companies can borrow from the open-source cloud playbook.(46:42) Casber highlighted the key trends he is most bullish on in the Open Data Ecosystem.(51:49) Casber emphasized the importance of interoperability in the modern data stack tooling landscape.(54:15) Casber painted the modular future of AI infrastructure.(59:44) Casber highlighted the key trends propelling the dynamic evolution of the software development lifecycle.(01:03:06) Casber reflected on his learning process for any new industry as an investor.(01:05:53) Closing segment.Casber's Contact InfoSapphire Ventures ProfileTwitterLinkedInMentioned ContentArticles3 Strategies Software Companies Can Borrow from the Open-Source Cloud Playbook (Aug 2020)What is the Open Data Ecosystem and Why It's Here to Stay (April 2021)The Future of AI Infrastructure is Becoming Modular: Why Best-of-Breed MLOps Solutions are Taking Off and Top Players to Watch (March 2022)Evolution of the Software Development Lifecycle and the Future of DevOps (June 2022)Books"The Power Law" (by Sebastian Mallaby)"Engines That Move Markets" (by Alasdair Naim)Notes
My conversation with Casber was recorded back in late 2022. Since then, I recommend checking out these resources:
Casber's appearance on Bloomberg NewsCasber's analysis of the next wave of cybersecuritySapphire's investments in Huntress and Weights & BiasesSapphire's new $1B fund to invest in AI-powered enterprise tech startups.About the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Show Notes(01:46) Itai reflected on his education at The Hebrew University of Jerusalem, studying Math and Computer Science.(04:18) Itai walked through his time as a software engineer at Google working in Google Trends.(06:56) Itai emphasized the importance of a software checklist within Google's engineering culture.(08:55) Itai explained how he became fascinated with AI/ML engineering.(10:31) Itai touched on his period working as an AI consultant.(13:28) Itai talked about his side hustle as a co-owner of Lia's Kitchen, a 100% vegan restaurant in Berlin.(16:13) Itai shared the founding story of Mona Labs, whose mission is to make AI and machine learning impactful, effective, reliable, and safe for fast-growth teams and businesses.(21:25) Itai unpacked the architecture overview of the Mona monitoring platform.(24:50) Itai talked about the early days of Mona finding design partners.(27:15) Itai dissected his perspective on a comprehensive monitoring strategy.(31:42) Itai explained why the secret to comprehensive monitoring lies in granular tracking and avoiding noise.(38:35) Itai explained how Mona can support real-time monitoring across the layers of the platform.(43:18) Itai mentioned the integration with New Relic to display the variability of use cases for Mona.(46:08) Itai discussed the shift for data science teams from being research-oriented to product-oriented.(51:03) Itai provided four tactics for data science teams to become "product-oriented."(58:04) Itai shared valuable hiring lessons to attract the right people who are excited about the mission of Mona Labs.(01:01:46) Itai provided his mental model for finding exceptional engineering talent.(01:03:38) Itai brought up again the importance of finding lighthouse customers.(01:05:46) Itai gave his thoughts on building the product to satisfy different customer needs.(01:07:55) Itai described the thriving ML engineering community in Israel.(01:09:42) Closing thoughtsItai's Contact InfoLinkedInMona Labs' ResourcesWebsite | LinkedIn | Twitter | YouTubeAbout | Customers | CareersPlatformBlog | Case Studies | DocsMentioned ContentBlog Posts and TalksWe are building Mona to bring ML observability to production AIThe definitive guide to AI/ML monitoringThe secret to successful AI monitoring: Get granular, but avoid noiseTaking AI from good to great by understanding it in the real world (June 2022)Data drift, concept drift, and how to monitor for themThe issues ML model retraining won't solveCommon pitfalls to avoid when evaluating an ML monitoring solutionIntroducing automated exploratory data analysis powered by MonaBest practices for setting up monitoring operations for your AI teamThe challenges of specificity in monitoring AIIs your LLM application ready for the public?Overcoming cultural shifts from data science to prompt engineeringPeopleGoku Mohandas (Creator of Made With ML)Ville Tuulos (CEO and Co-Founder of Outerbounds)Nimrod Tamir (CTO and Co-Founder of Mona Labs)Notes
My conversation with Itai was recorded back in October 2022. Since then, Mona Labs has introduced a new self-service monitoring solution for GPT! Read Itai's blog post for the technical details.
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Show Notes(01:33) Gabi shared her professional interests growing up - from painting and drawing to graphic design.(04:44) Gabi touched on her entrance to the field of data visualization.(06:30) Gabi described her graduate school experience studying Data Visualization at Parsons School of Design.(08:50) Gabi talked about the benefits of teaching data visualization classes later in her career.(12:30) Gabi recalled working as a Data Visualization specialist at The Washington Post.(14:50) Gabi gave her perspective on the evolution of data journalism.(18:29) Gabi talked about her experience co-founding Raw Haus, a creative community bringing together emerging talent in design, technology, and entrepreneurship.(21:20) Gabi emphasized the magic of community gatherings.(23:51) Gabi reflected on her time at WeWork as a senior data visualization engineer to design and build graphics, dashboards, and tools that tell stories using data.(28:02) Gabi walked through the evolution of the Data Cult initiative - which she co-created with Leah Weiss.(31:44) Gabi recalled her decision to leave WeWork in early 2020 and start Data Culture - a data engineering and visualization consultancy focused on helping organizations build data capabilities, implement modern infrastructure and create lasting data culture.(36:31) Gabi unpacked Data Culture's well-defined blueprint for each client engagement.(39:21) Gabi explained how Data Culture leveraged tools in the modern data stack for its consulting services.(40:42) Gabi brought up Data Culture's Studio - which offers data storytelling and visualization services to mission-aligned organizations.(42:50) Gabi reviewed her experience working with Kode with Klossy to empower young scholars to solve important issues using data science.(46:13) Gabi shared her perspective on how companies can scale their respective data cultures.(49:52) Gabi shared the story behind the founding of Preql.(52:59) Gabi touched on the process of working with design partners for Preql.(56:42) Gabi shared valuable hiring lessons to attract the right people at Data Culture and Preql.(58:10) Gabi provided her perspective on building a diverse team.(01:00:57) Gabi shared fundraising advice to data founders who are seeking the right investors for their startups.(01:03:34) Closing segment.Gabi's Contact InfoLinkedInTwitterPreql's ResourcesWebsite | Twitter | LinkedInIntroducing Preql: The Future of Data Transformation (April 2022)Mentioned ResourcesPeopleUmi Syam (Graphics and Multimedia Editor at the New York Times)Giorgia Lupi (Information Designer and Partner at Pentagram)Susie Lu (Senior Data Visualization Engineer at Netflix)BookInvisible Women: Exploring Data Bias in a World Designed for Men (by Caroline Criado Perez)Notes
My conversation with Gabi was recorded back in August 2022. Since then, Preql has officially launched and currently supports strategy and operations teams at B2B and vertical SaaS companies!
About the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Show Notes(01:55) Alex reflected on his upbringing as an immigrant moving from Colombia to the US at 14.(07:06) Alex recalled his undergraduate experience at NYUâs Polytechnic School of Engineering. where he study Computer Science and do research in cryptography.(16:40) Alex went over his first job working as a software engineer at FactSet Research System.(20:13) Alex walked through his time as the first employee and the first engineer at YieldMo.(24:30) Alex talked about his hiring philosophy for engineers who care about their craft.(28:03) Alex touched on the backstory behind the creation of Concord, with Shinji Kim and Robert Blafford, while working at YieldMo.(32:26) Alex shared lessons learned from his first-time founder experience with Concord.(35:22) Alex went over his two years at Akamai as a Platform Infrastructure Engineer after the Concord acquisition.(40:01) Alex introduced his work on SMF, an RPC framework designed for microsecond tail latency.(43:41) Alex shared the story behind the founding of Redpanda Data, which builds a high-performance, Apache Kafka-compatible data streaming platform for mission-critical workloads.(47:19) Alex walked through the major benefits of choosing Redpanda over Kafka.(51:03) Alex explained his decision to open-source Redpanda in November 2020 under the Source Available License BSL.(56:08) Alex mentioned successful tactics his team employed in order to raise the adoption and contribution to the open-source library.(01:01:13) Alex unpacked the design of Redpanda's Intelligent Data API.(01:08:55) Alex provided his perspective on the modern streaming data architecture.(01:13:24) Alex shared valuable hiring lessons to attract the right people who are excited about Redpandaâs mission.(01:18:30) Alex talked about his experience choosing customers for Redpanda.(01:20:33) Alex shared fundraising advice to founders who are seeking the right investors for their startups.(01:23:23) Alex gave advice to a smart, driven minority who aspires to work on ambitious, technically deep, and challenging problems.(01:28:18) Closing segment.Alex's Contact InfoLinkedInTwitterWebsiteGitHubRedpanda's ResourcesWebsite | Twitter | LinkedIn | Slack | GitHub | Contributing DocAbout Redpanda | Platform Capabilities | CustomersDocs | Redpanda UniversityReports and Guides | BenchmarksHack The Planet ScholarshipMentioned ContentBlog PostsRedpanda raison d'etre (Feb 2019)Thread-per-core buffer management for a modern Kafka-API storage system (Sep 2020)Redpanda is now free and Source Available (Nov 2020)Redpanda creates Redpanda, the Intelligent Data API Platform, backed by $15.5M initial funding from Lightspeed Venture Partners and GV (Jan 2021)The Intelligent Data API (Jan 2021)Redpanda Wasm engine architecture (June 2021)We raised an additional $50M to drive the future of streaming data. Join us! (Feb 2022)Redpanda gives Kafka a Run for Its Money (InfoWorld, May 2022)Alex Gallego Builds Redpanda To Simplify And Unify Real-Time Streaming Data (Forbes, June 2022)TalksDistributed Stream Processing over thousands of Datacenters (GeeCON, Aug 2017)How to Build the Fastest RPC (Nov 2017)Co-designing Raft + thread-per-core execution model for the Kafka-API (Dec 2021)PeopleAndy PavloLeslie LamportKyle KingsburyNotes
My conversation with Alex was recorded back in August 2022. Since then, I recommend checking out these resources:
The $100M Series C funding announcementThis guide for developers on streaming dataCustomer case studies with Lacework, Exein, and SmartLunchResources on the advantage of Redpanda over Apache Kafka (cost of ownership comparison, data sovereignty, and this holistic comparison)About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Show Notes(01:44) Chetan reflected on his undergraduate experience at Stanford studying Electrical Engineering and Statistics back in the late 2000s.(06:10) Chetan recalled his experience interning at IBM and Quantcast and doing research at Stanford Center for Minds, Brain, and Computation.(08:41) Chetan talked about his first job working as a research analyst focused on healthcare policy at Acumen.(11:15) Chetan walked through his decision to join Airbnb as their 4th data scientist and work on building Airbnb's original ETL framework for online risk mitigation.(15:12) Chetan recalled the early state of data science at Airbnb.(18:17) Chetan touched on the development of Airbnb's knowledge management and sharing platform called Knowledge Repo.(23:10) Chetan explained why an experimentation program is the most impactful thing a data team can do.(26:06) Chetan walked through the evolution of Airbnb's experimentation platform since its inception in 2014.(31:24) Chetan recalled fond memories from taking a year off from work to travel.(35:16) Chetan touched on his transition back to work by way of living in Atlanta and co-founding a logistics software startup called Saltbox.(39:28) Chetan described his time as a data scientist at Webflow, building their experimentation system from scratch.(42:48) Chetan shared the story behind the founding of Eppo.(46:12) Chetan dissected the key capabilities that are baked into the Eppo product.(48:32) Chetan dived deeper into the problems caused by long experiment durations and the benefits of using CUPED to bend time in experiments.(52:04) Chetan talked about the role of a statistics engineer.(54:45) Chetan shared his perspective on the role of experimentation in the Modern Data Stack and the Modern Growth Stack.(01:00:47) Chetan discussed the core elements of the modern experimentation stack.(01:04:54) Chetan talked about the experiment overhead.(01:06:24) Chetan emphasized the designer gap in experimentation tools(01:08:50) Chetan shared his thoughts about metric strategy.(01:10:51) Chetan shared valuable hiring lessons to attract the right people who are excited about Eppo's mission.(01:14:10) Chetan provided his perspectives on finding design partners for an early-stage startup.(01:17:04) Chetan shared fundraising advice to founders who are seeking the right investors for their startups.(01:19:40) Closing segment.Chetan's Contact InfoLinkedInTwitterGitHubAngelListEppo's ResourcesWebsite | Twitter | LinkedInBlog | Updates | DocAbout | CareersExperimentation ProductFeature Flagging ProductMentioned ContentArticlesTravel Year Facts and Superlatives (Dec 2019)Why I Started Eppo (Feb 2021)Reducing Experiment Durations (June 2021)The Designer Gap in Experimentation Tools (June 2021)We're Hiring A Statistics Engineer! (Aug 2021)Should You Always Run An Experiment? (Aug 2021)Stop Micromanaging Product Strategy (Sep 2021)The most impactful thing a data team can do is establish an experimentation program (Dec 2021)Bending Time in Experimentation (June 2022)We Raised $19.5M! (June 2022)Experimentation for the Modern Growth Stack: Our Investment in Eppo (June 2022)PeopleMike KaminskySean TaylorJeremy HowardBookThe Mom's Test (by Rob Fitzpatrick)Notes
My conversation with Chetan was recorded back in August 2022. Since then, Eppo has launched feature flagging, and now offers the first "flags on top of your warehouse" experimentation platform. They also have Miro, Twitch, DraftKings, and Zapier as customers.
About the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Show Notes(01:38) Chad reflected on his early career as a freelance journalist working in Southeast Asia.(04:27) Chad explained the benefits of writing for anyone working in a technical field.(06:15) Chad touched on his entrance to data analytics through Conversion Rate Optimization.(09:06) Chad walked through his decision to dive deep into the field of experimentation.(13:28) Chad recalled how he spent time learning the basics of statistics.(15:32) Chad discussed the differences in experimentation cultures at Subway, SEPHORA, and Microsoft.(19:11) Chad shared the technical details behind the evolution of Convoy's data platform since he joined in 2019.(23:05) Chad emphasized the role of data in Convoy's digital freight business.(26:17) Chad brought up the importance of solving data discovery at Convoy and their decision to choose Amundsen.(29:02) Chad shared lessons learned setting up a flexible experimentation platform at Convoy.(32:46) Chad unpacked the problems with Change Data Capture and how his team built an internal change management platform called Chassis (a source of truth for definitions of events, entities, and relationships.).(41:33) Chad discussed the existential threat of data quality.(44:38) Chad explained why the modern data warehouse is broken and why the "Immutable Data Warehouse" can be a solution.(51:39) Chad zoomed in on the death of data modeling.(57:42) Chad is bullish on the rise of the knowledge layer and data contracts in the upcoming years.(01:03:37) Chad talked at length about the data collaboration problem.(01:09:28) Chad gave advice for data organizations to be more customer-centric.(01:11:55) Chad shared components of a high-quality Data UX function that any centralized data team should consider when developing data experiences.(01:14:46) Chad touched on his mental framework for evaluating potential investments in the data space.(01:18:57) Chad brought up the valuable skills he acquired as an internal product manager.(01:20:05) Closing segment.Chad's Contact InfoLinkedInData Products SubstackData Quality CampMentioned ContentTalksAligning Experimentation Across Product Development and Marketing (CXL Live 2019)Chassis: Entities, Events, and Change Management (Data Quality Meetup, 2021)1,000 Experiments Club with AB Tasty (July 2021)Data Discovery at Lyft and Convoy (July 2021) (with Mark Grover)The growth of the data platform product manager role (The Tech Trek, Dec 2021)Implementing Amundsen at Convoy (Building the Backend, Jan 2022)Getting ROI from Experimentation: How AB Experimentation plays out in Organizations (Data Council, March 2022)Why are we so bad at this modern data stack? (Catalog and Cocktails, April 2022)ArticlesExperimentation not only protects your KPIs but your job as well (Dec 2019)Is The Modern Data Warehouse Broken? (April 2022) (with Barr Moses)The Existential Threat of Data Quality (May 2022)The Death of Data Modeling (June 2022)Data Collaboration Problem (June 2022)The Rise of Data Contracts (Aug 2022)PeopleBarr Moses (Monte Carlo Data)Juan Sequeda (data.world)Adrian Kreuziger (Convoy)BookAgile Data Warehouse Design (by Lawrence Corr)Notes
My conversation with Chad was recorded back in July 2022. Since then, I'd recommend looking at:
His two-part series on engineering guide to data contracts (Part 1 and Part 2)The Data Quality Camp communityHis most recent post on how scale kills data teamsData Facade (of which he is an angel investor)About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Show Notes(01:56) Curtis reflected on his upbringing in rural Kentucky and his gift of education.(07:20) Curtis explained how he cultivated mental focus and intellectual fortitude while growing up in Kentucky.(10:30) Curtis shared his view regarding online misinformation on social media.(14:27) Curtis recalled his undergraduate experience at Vanderbilt University in the early 2010s.(22:39) Curtis explained how he learned best via teaching and mentoring.(24:04) Curtis walked through the research and industry experiences he obtained throughout college.(32:45) Curtis recalled his decision to embark on a Ph.D. in Computer Science at MIT.(38:53) Curtis told the story of how he ended up finding his advisor - Professor Isaac Chuang (the inventor of the first working quantum computer).(40:36) Curtis mentioned how he invented the CAMEO Detection Algorithm to detect âmultiple-accountâ cheating in massive open online courses.(44:47) Curtis unpacked his Ph.D. research on dataset uncertainty estimation.(50:08) Curtis dissected confident learning, a family of theories and algorithms for supervised ML with label errors.(53:22) Curtis encapsulated how he strategically iterated cleanlab at his various graduate internships.(01:00:22) Curtis recalled his time founding his first startup ChipBrain, before founding Cleanlab.(01:06:42) Curtis brought up the creation of the labelerrors.com project.(01:12:12) Curtis provided lessons learned as a second-time founder.(01:14:25) Curtis elaborated on the open-source roadmap of cleanlab.(01:17:08) Curtis highlighted the key capabilities of Cleanlab Studio - the no-code, automatic data correction solution for data and engineering teams with robust enterprise features.(01:18:50) Curtis touched on Cleanlab Vizzy - an interactive visualization of confident learning.(01:20:29) Curtis shared valuable hiring lessons to attract the right people who are excited about Cleanlabâs mission.(01:23:23) Curtis gave his thoughts on shaping Cleanlabâs culture.(01:26:06) Curtis explained the similarity and differences between being a founder and a researcher.(01:29:09) Curtis mentioned how he had helped researchers build affordable state-of-the-art deep learning machines.(01:31:46) Curtis brought up his alter ego PomDP the Ph.D. rapper, and how rapping has been an outlet for him to express emotions and creativity.(01:40:12) Curtis emphasized how his success had been due to a function of grit, resourcefulness, and friends made along the way.(01:44:04) Closing segment.Curtis' Contact InfoAcademic WebsiteLinkedIn | Twitter | Facebook | InstagramGoogle Scholar | GitHubPhD Rapper (YouTube | Spotify | SoundCloud | Facebook | Twitter | Instagram)L7 Machine Learning BlogCleanlab's ResourcesWebsite | GitHub | Slack | Twitter | LinkedInBlog | Research | DocAbout | CareersCleanlab StudioCleanlab VizzyThe Cleanlab CultureMentioned ContentPapersDetecting and preventing âmultiple-accountâ cheating in massive open online courses, Curtis G. Northcutt, Andrew Ho, & Isaac L. Chuang, Computers & Education, 2016. [paper | code | arXiv]Comment Ranking Diversification in Forum Discussions, Curtis G. Northcutt, Kimberly Leon, & Naichun Chen, Learning at Scale, 2017. [paper | code | free-access]Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels, Curtis G. Northcutt, Tailin Wu, & Isaac L. Chuang, 33rd Conference on Uncertainty in Artificial Intelligence (UAI 2017). [paper | code]Confident Learning: Estimating Uncertainty for Dataset Labels, Curtis G. Northcutt, Lu Jiang, & Isaac L. Chuang, Journal of Artificial Intelligence Research (JAIR), Vol. 70 (2021). [paper | code | blog]Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks, Curtis Northcutt, Anish Athalye, and Jonas Mueller, 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks [paper| demo | code | blog]Blog PostsFounderâs Medal recipient chooses MIT over Microsoft (May 2013)Build a Pro Deep Learning Workstation... for Half the Price (Feb 2019)An Introduction to Confident Learning: Finding and Learning with Label Errors in Datasets (Nov 2019)Announcing cleanlab: a Python Package for ML and Deep Learning on Datasets with Label Errors (Nov 2019)Double Deep Learning Speed by Changing the Position of your GPUs (Dec 2019)Benchmarking: Which GPU for Deep Learning? (Dec 2019)The Best 4-GPU Deep Learning Rig only costs $7000 not $11,000 (April 2020)Pervasive Label Errors in ML Datasets Destabilize Benchmarks (March 2021)Cleanlab: The History, Present, and Future (April 2022)cleanlab 2.0: Automatically Find Errors in ML Datasets (April 2022)How We Built Cleanlab Vizzy (August 2022)Talks and PodcastsTedx Talk: The MIT Rap Challenge (July 2020)Talk at NLP Summit (March 2022)Talk at Data + AI Summit (June 2022)MLOps Coffee Chat (July 2022)Talk at Snorkel's Future of Data-Centric AI Conference (July 2022)Open-Source Startup Podcast (March 2023)PeopleLeslie KaelblingGeoff HintonJeff DeanBookPlay Bigger: How Pirates, Dreamers, and Innovators Create and Dominate Markets (by Al Ramadan, Dave Peterson, Chris Lockhead, and Kevin Maney)Notes
My conversation with Curtis was recorded back in August 2022. The Cleanlab team has had some important announcements in 2023 that I recommend looking at:
The launches of CleanVision, Datalab, and ActiveLabThis blog post on using Cleanlab to improve LLMsHis new single "Clarity In My Vision"Cleanlab's partnership with Databricks (Video)Cleanlab is about to announce its Series A announcement soon. Stay on the look for it!
About the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Show Notes(01:41) Frank shared formative experiences of his upbringing moving from China to the US.(04:45) Frank described his overall academic experience at Stanford, studying Electrical Engineering with a minor in Computer Science.(08:41) Frank talked about his research and industry experience while at Stanford.(11:34) Frank shared his proudest accomplishments working at Yahoo as a research engineer in the Vision and Machine Learning group.(16:37) Frank went over his experience co-founding a company that developed indoor localization and navigation solutions called Orion.(23:06) Frank walked through his decision to leave Silicon Valley for China.(26:02) Frank talked about his experience living and doing business in China (check out his two-part blog series that has covered normal life and the pandemic story in China).(32:44) Frank elaborated on the work culture differences between the East and the West.(37:58) Frank reflected on his decision to join Zilliz back in August 2021.(42:55) Frank unpacked the notion of vector databases for the un-initiated.(47:44) Frank provided a brief overview on the high-level design of Milvus, Zilliz's advanced open-source vector database solution.(51:38) Frank highlighted three unique use cases of Milvus - malware detection, reverse image search, and drug discovery.(56:51) Frank introduced Towhee, an open-source project that helps software engineers develop and deploy applications that utilize embeddings in just a few lines of code.(01:01:59) Frank anticipated the evolution of the embedding tooling landscape to support the increasing adoption of unstructured data.(01:04:21) Frank gave a primer on Zilliz Cloud, Zilliz's enterprise vector database solution.(01:06:30) Closing segment.Frank's Contact InfoLinkedInTwitterGitHubWebsiteZilliz's ResourcesWebsite | Twitter | LinkedIn | GitHub | YouTubeZilliz Cloud DatabaseMilvus (Docs | GitHub)Towhee (Docs | GitHub)Mentioned ContentArticles and PresentationsA Gentle Introduction to Vector Databases (Dec 2021)My Experience Living and Working in China, Part I (Feb 2022)My Experience Living and Working in China, Part II (March 2022)Making ML More Accessible for Application Developers (April 2022)Understanding Neural Network Embeddings (April 2022)Building An Open-Source Platform for Generating Embedding Vectors (Berlin Buzzwords, 2022)PeopleYann LeCun (Chief AI Scientist at Meta, Professor at NYU)Yangqing Jia (Creator of the Caffe deep learning framework)Soumith Chintala (Creator of the PyTorch deep learning framework)BookA Short History of Nearly Everything (by Bill Bryson)Notes
My conversation with Frank was recorded back in August 2022. The Zilliz team has had some important announcements in 2023 that I recommend looking at:
The landing page of Zilliz CloudThe beta launch of Milvus 2.3The development of GPTCacheThe OSS Chat demo applicationAbout the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Show Notes(01:58) Vinoth shared his college experience studying IT at the Madras Institute of Technology in Chennai, India.(07:09) Vinoth reflected on his time at UT Austin, getting a Master's degree in Computer Science - where he did research on high-bandwidth content distribution and large-scale parallel processing with shell pipes.(11:20) Vinoth recalled his two years as a software engineer at Oracle, working on their database replication engine, HPC, and stream processing.(15:30) Vinoth walked over his transition to LinkedIn as a senior software engineer, working primarily on Voldemort - a key-value store that handles a big chunk of traffic on Linkedin and serves thousands of requests per second over terabytes of data.(24:41) Vinoth talked about his career transition to Uber in late 2014 as a founding engineer on Uber's data team and architect of Uber's data architecture.(28:39) Vinoth reflected on the state of Uber's data infrastructure when he joined.(34:31) Vinoth elaborated on Uber's case for incremental processing on Hadoop.(38:53) Vinoth reviewed the initial design and implementation of Hudi across the Hadoop ecosystem at Uber in 2016.(41:33) Vinoth shared the evolution of Hudi after it was initially open-sourced by Uber in 2017 and eventually incubated into the Apache Software Foundation in 2019.(46:49) Vinoth explained how to keep the development of Apache Hudi vendor-neutral.(49:36) Vinoth provided lessons learned about establishing standards for open-source data projects.(53:45) Vinoth went over the valuable leadership lessons that he absorbed throughout his 4.5 years at Uber.(57:17) Vinoth reflected on his 1.5 years as a principal engineer at Confluent working on ksqlDB, which makes it easy to create event streaming applications.(01:02:16) Vinoth articulated the vision for Apache Hudi as a Streaming Data Lake platform.(01:08:00) Vinoth highlighted the challenges with databases around indexing and concurrency control.(01:11:37) Vinoth shared the unique challenges around prioritizing the Hudi roadmap and engaging an open-source community.(01:16:32) Vinoth shared the founding story of Onehouse, a cloud-native, fully-managed lakehouse service built on Apache Hudi.(01:22:02 ) Vinoth emphasized Onehouse's commitment towards openness.(01:24:36) Vinoth shared valuable hiring lessons to attract the right people who are excited about Onehouse's mission.(01:26:40) Vinoth shared fundraising advice to founders who are seeking the right investors for their startups.(01:28:24) Closing segment.Vinoth's Contact InfoLinkedInTwitterOnehouse's ResourcesWebsite | Twitter | LinkedInAbout | Product | Blog | CareersApache Hudi's ResourcesUser Docs | Technical Wiki | RoadmapGitHub | Twitter | SlackMentioned ContentArticles and PresentationsVoldemort : Prototype to Production (May 2014)Uber's Case for Incremental Processing on Hadoop (Aug 2016)Hoodie: An Open Source Incremental Processing Framework From Uber (2017)The Past, Present, and Future of Efficient Data Lake Architectures (2021)Highly Available, Fault-Tolerant Pull Queries in ksqlDB (May 2020)Apache Hudi - The Data Lake Platform (July 2021)Introducing Onehouse (Feb 2022)Automagic Data Lake Infrastructure (Feb 2022)Onehouse Commitment to Openness (Feb 2022)PeopleLeslie LamportJeff DeanMichael StonebreakerBookZero To One (by Peter Thiel)Notes
My conversation with Vinoth was recorded back in August 2022. The Onehouse team has had some announcements in 2023 that I recommend looking at:
The Launch Announcement of OnetableThe $25M Series A Funding AnnouncementOnehouse Availability in AWS MarketplaceOnehouse Product Demo on building a data lake for GitHub analytics at scaleWalmart's recent study on different open-source data lakehouse formatsThis discussion around the Hudi 1.x visionAbout the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Show Notes(01:55) Alexa shared formative experiences of her upbringing in Philadelphia.(03:47) Alexa reflected on her undergraduate experience at Vanderbilt studying Engineering Science.(05:49) Alexa recalled her first job out of college in management consulting at KPMG.(08:20) Alexa walked over her transition from consulting to technology when she joined the Sales Operations team at Dataminr.(12:35) Alexa talked about her proudest accomplishments at Dataminr - seeding the initial idea for Pocus and building a community for women in the workplace.(20:23) Alexa reflected on her MBA experience at the Stanford Graduate School of Business.(24:05) Alexa elaborated on the mindset difference between investing and operating.(25:27) Alexa briefly touched on her internship at Monte Carlo.(27:58) Alexa shared the founding story of Pocus.(32:27) Alexa unpacked the concept of Product-Led Sales as a GTM approach.(35:40) Alexa provided two example use cases of Pocus.(39:35) Alexa explained the concepts of Product-Qualified Leads and Sales-Assist.(42:20) Alexa discussed the long-term vision of Pocus' product roadmap.(45:33) Alexa shared valuable hiring lessons to attract the right people who are aligned to Pocus' values.(51:15) Alexa went over the journey of building the Product-Led Sales community.(54:54) Alexa shared the unique opportunities of evolving a category, a community, and a product all at once.(57:56) Alexa shared fundraising advice to founders who are seeking the right investors for their startups.(01:01:15 ) Alexa provided advice to a smart, driven female operator who wants to take the leap of founding her company.(01:03:09) Closing segment.Alexa' Contact InfoLinkedInTwitterPocus' ResourcesWebsite | Twitter | LinkedIn | YouTubeAbout | Product | Blog | CareersCommunity | NewsletterMentioned ContentBlog PostsWhat is Product-Led Sales? (July 2022)The Myth of "No Sales" at PLG Companies (July 2021)When To Add A Sales Team to Your PLG Company (Sep 2021)The Definitive PQL Guide: Part 1, Part 2, Part 3 (Nov 2021)What Is The Sales-Assist Role? (Nov 2021)Introducing Pocus' PLS Platform (Nov 2021)Product-Led Sales Community Wisdom Highlights 2021 (Dec 2021)Notes on Community-Led Category Creation with Pocus' Co-Founder, Alexa Grabell (Feb 2022)Sneak Peek at Pocus' PLS Platform (March 2022)Announcing $23M to Transform How GTM Teams Use Data to Drive Revenue (June 2022)Year One: The Product-Led Sales Platform is Here to Stay (July 2022)PeopleKyle Poyar (OpenView Ventures)Melissa Ross (Clockwise)Aaron Geller (QuickNode)Notes
My conversation with Alexa was recorded back in July 2022. The Pocus team has had some announcements in 2023 that I recommend looking at:
The launch announcement of Pocus' Revenue Data PlatformThe Product-Led Sales Playbook Volume 2The Unlocking Revenue podcastThe Playbook Library for product-led go-to-marketAbout the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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Show Notes(02:06) Carlos shared formative experiences of his upbringing tinkering with robots and websites.(04:03) Carlos reflected on his education, studying Mechanical and Aerospace Engineering at Cornell University.(05:34) Carlos discussed the technical details of his research on machine learning applications in robotics and art.(10:11) Carlos explained his work as a robotic system analyst at Kiva Systems.(15:41) Carlos discussed building his first data product at Kiva.(20:24) Carlos recalled his stint working on warehouse-automating distributed robots at Amazon Robotics (after the Kiva acquisition).(24:31) Carlos revealed his decision in 2013 to join an early-stage healthcare startup called Flatiron Health as the first data hire.(28:43) Carlos shared his experience building Flatiron's Data Insights team from scratch.(31:51) Carlos reviewed different data products built and deployed at Flatiron Health.(38:41) Carlos shared the key learnings from hiring for his data team at Flatiron.(44:08) Carlos shared the founding story of Glean, which is building a new way to make data exploration and visualization accessible to everyone.(50:52) Carlos explained the pain points in data visualization/exploration and the product features of Glean that address them.(55:03) Carlos dissected Glean DataOps, which brings modern developer workflow to the business intelligence layer and prevents broken dashboards.(59:28) Carlos outlined the long-term product vision for Glean.(01:03:11) Carlos shared valuable hiring lessons to attract the right people who are excited about Glean's mission.(01:07:15) Carlos discussed his team's challenges in finding the early design partners.(01:10:13) Carlos shared fundraising advice to founders who are seeking the right investors for their startups.(01:11:57) Closing segment.Carlos' Contact InfoTwitterLinkedInGitHubWebsiteMediumGlean's ResourcesWebsite | Twitter | LinkedInAbout | Docs | BlogInteractive Public Demo | DataOpsMentioned ContentBlog PostsHow the Data Insights team helps Flatiron build useful data products (May 2018)The biggest mistake making your first data hire: not interviewing for product (July 2020)How to interview your first data hire (Aug 2020)My hack for getting started with data as a product (May 2021)Introducing Glean (March 2022)Your dashboard is probably broken (April 2022)PeopleVicki BoykisAnthony GoldbloomWes McKinneyBookThe Toyota Way: 14 Management Principles from the World's Greatest Manufacturer (by Jeffrey Liker)Notes
My conversation with Carlos was recorded back in June 2022. The Glean team has had some announcements in 2023 that I recommend looking at:
The recently launched, interactive public demo siteThis recent integration with DuckDBThis post about Version Control for BITheir Public RoadmapAbout the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths â from scientists and analysts to founders and investors â to analyze the case for using data in the real world and extract their mental models (âthe WHY and the HOWâ) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].
Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:
Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf youâre new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.
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