Episódios
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In this DSS Podcast we chat with Matthew Denesuk, SVP of Data Analytics & AI at Royal Caribbean Group. Matthew shares his insights on leveraging a Center of Excellence model to drive data-driven strategies across the organization. Tune in to discover how this approach can transform enterprise business processes using AI, analytics, and data science!
We’re also excited to welcome Jaime Russ, former Principal Data Scientist at Ryder System. Jaime brings a fresh perspective on data science, focusing on integrating advanced analytics and machine learning models into traditionally held concepts. Tune in as she explores the application of machine learning in corporate finance and its fascinating parallels to fleet management. -
In this DSS Podcast, Anna Anisin welcomes Serg Masís, Climate and Agronomic Data Scientist at Syngenta. Serg, an expert in machine learning interpretability and responsible AI, shares his diverse background and journey into data science. He discusses the challenges of building fair and reliable ML models, emphasizing the importance of interpretability and trust in AI. Serg also talks into his latest book, "Interpretable Machine Learning with Python," and provides valuable insights for data scientists striving to create more transparent and effective AI solutions.
In another compelling episode, Anna sits down with Nirmal Budhathoki, Senior Data Scientist at Microsoft. Nirmal, who has extensive experience at VMware Carbon Black and Wells Fargo, focuses on the intersection of AI and cybersecurity. He shares his journey into security data science, discussing the unique challenges and critical importance of applying AI to enhance cybersecurity measures. Nirmal highlights the pressing need for AI in this field, practical use cases, and the complexities involved in integrating AI with security practices, offering a valuable perspective for professionals navigating this dynamic landscape. -
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In this week's DSSPodcast, Anna had a conversation with Boshika Tara, Technical Machine Learning Product Manager at H&M Group. Boshika brings over 7 years of experience in technical product development, engineering, and building large-scale ML systems in NLP and Computer Vision. In this episode, she dives into the critical issue of bias in AI, discussing various types of biases in machine learning, how to detect them, and the importance of creating more equitable teams with diverse representation to mitigate these biases.
Additionally, Anna had the pleasure of hosting Dr. June Andrews, the Founder of Lat Long Labs. Dr. Andrews shares her incredible journey from leading the Style Discovery team at Stitch Fix to her role as a Tech Lead at LinkedIn. She discusses the complexities of scaling and transforming AI projects, particularly in predicting consumer preferences and enhancing product discovery. -
In this episode of the DSS Podcast, Anna Anisin introduces two powerhouse guests in the realms of AI and robotics.
First, Anna welcomes Alex, Principal Algorithms/AI Engineer at Elbit Systems of America, based in Miami. Alex shares her journey into the field of AI, particularly computer vision, and discusses common use cases, pitfalls, and success stories in sourcing and improving data for computer vision models. She also offers valuable recommendations for data scientists starting out in the field and highlights an exciting trend in AI that she's currently following.
Next, Anna introduces Sheila Beladinejad, President of Women in AI & Robotics. Sheila talks about the network she built in Germany, dedicated to fostering gender-inclusive, ethical, and responsible AI and robotics solutions. She highlights the importance of creating such a network and the positive impact it has had on the AI and robotics community. -
In this episode of the Data Science Salon Podcast, host Anna Anisin sits down with two leading experts in the ML/AI healthcare industry. First, Sumayah Rahman, Director of Data Science - Machine Learning and Infrastructure at Cedar, discusses optimizing the patient experience to make healthcare more affordable and accessible. She explains how ML-powered discounts can benefit both patients and providers, sharing practical examples of using data to enhance patient experiences and highlighting the transformative impact of AI/ML in healthcare.
Next, Vaibhav Verdhan, Analytics Leader at AstraZeneca, dives into the role of computer vision in healthcare and his favorite technologies in the healthcare analytics space. He discusses how advanced analytics are driving innovation at AstraZeneca by developing, deploying, and maintaining decision support capabilities. Both guests provide valuable insights into how AI and ML are revolutionizing healthcare, offering listeners practical knowledge and inspiration. -
In this episode, Anna sits down with two leaders in the finance industry, exploring the forefront of AI and ML innovations.
First, we have Mabu Manaileng, Lead Data Scientist at Standard Bank Group. Mabu shares his journey and current role, highlights the challenges of applied data science in the financial sector, and discusses the transformative impact of AI on banking in the coming years.
Next, we welcome Adam Lieberman, Head of AI and ML at Finastra. Adam defines the concept of drift, discusses statistical measures to quantify it, and provides strategies for maintaining model health, ensuring that models continue to serve users' needs effectively. -
In this episode, Anna sits down with two leaders in the finance industry, exploring the forefront of AI, ML, and ESG innovations.
First, let's welcome Laura Gabrysiak, Data Science Leader at Visa. Laura develops statistical models and decision analytics tools that enable Visa clients to transform massive amounts of data into actionable ML models and AI implementations. She's also passionate about fostering the local data science community in Miami as the Founder of R-Ladies Miami. In this conversation, they dive into the future of ML/AI in financial services and the impactful work being done with Code Art to promote diversity in tech.
Next, we have Rochelle March, former Head of ESG Product at Dun & Bradstreet. Rochelle specializes in impact analysis related to carbon, water, and the Sustainable Development Goals, and applies machine learning to ESG products. She also teaches data and analytics at Bard College’s MBA program, sits on the advisory board for USL Technology, Inc., and mentors fellows in the Environmental Defense Fund’s Climate Corps program. Since recording this episode, Rochelle has started her own company, People Places Words Actions. In our discussion, we explore her journey in ESG innovation and analytics, why ESG data is crucial for responsible investment decisions, and how it drives sustainable business practices.
Tune in to learn from these industry thought leaders and gain insights into the cutting-edge applications of AI and ESG data in the finance sector. -
In this episode, Anna sits down with two distinguished leaders in the ML/AI finance industry. First, we have Harry Mendell, Technology Group Data Architect at the Federal Reserve Bank of New York, who brings over 30 years of expertise in FinTech. Harry shares compelling stories and discusses emerging trends in the finance sector.
Following Harry, Supreet Kaur, AVP at Morgan Stanley and product owner for various AI products, joins the conversation. Supreet provides insights into the use of synthetic data to protect customer privacy in FinTech, ensuring informed decision-making. This deep dive into synthetic data highlights its growing importance in the industry. -
Ben Dubow studied the Middle East during his undergrad and took a job tracking terrorist groups. After a brief stint at a large tech company, he launched Omelas, a company that combines AI and subject matter expertise to deliver intelligence to national security professionals.In today's episode, our Senior Content Advisor Q McCallum caught up with Ben to learn more about what Omelas is up to and how the company applies AI and data analysis to its mission.Along the way they explore the value of data in context; why it's important to ask the right questions of the right data, and not just the whole pool; the power of involving humans in the data pipeline; and what it takes to do NLP and NER at scale. The two also talk about the impact of generative AI on democracy and authoritarianism. A topic which, interestingly enough, holds lessons for corporations that plan to release AI chatbots.Links mentioned in this episode:
Ben's LinkedIn profileOmelas websiteBen's writing on the Center for European Policy Analysis (CEPA) websiteArticle in Les Echos describing the project "Le Monde in English": "« Le Monde » parie sur l'étranger pour stimuler sa croissance"Q's write-up on "Risk Management for Generative AI Bots" is available on both his O'Reilly Radar page and his blog. -
If you've been in the data game long enough, you've probably seen this before: a stakeholder or product owner approaches you with a project that's 95% done, and they'd like you to … "sprinkle some AI on it." They've heard that this "AI" thing can be useful so they want some of it in their latest effort.Data scientist-turned-product person Noelle Saldana has experienced the "sprinkle some AI on it" request more times than she'd care to remember. Our Senior Content Advisor Q McCallum met up with Noelle to explore this phenomenon. How does this happen? (Hint: "corporate FOMO.") What should you do when stakeholders insist on implementing AI that isn't actually going to help? What about when your data scientist peers seem like they're doing this for the sake of "résumé-driven development?"Ultimately, the pair work through the bigger issue: how do you make peace with companies throwing money at AI like this? And how can these companies use this approach to their advantage?As a bonus, Noelle shares how she made the move from a data scientist role into product management. If this path sounds interesting to you, take a listen.
Noelle's Data Council talk, "Hot Takes and Tragic Mistakes: How (not) to Integrate Data People in Your App Dev Team Workflows"Find Noelle on LinkedIn: https://www.linkedin.com/in/noellesio/Q's blog post (which came out much better thanks to Noelle's help): "AI isn't something you just add to a company" -
Sometimes the most valuable data IN your company ... is the data LEAVING your company.That's Solomon Kahn's view on data products, as well as the premise behind his latest venture: Delivery Layer.For this episode, our Senior Content Advisor Q McCallum reached out to Solomon to check in on the new startup, and to tap his expertise in the world of data products.Solomon's been at this a while. He's run high-revenue data products in some notable places, including Nielsen. Over the years he's learned a lot and we're excited for him to share some of that hard-earned knowledge here on the show.In this extended conversation, the two explore: the reasons why building a data product is different (and, in many ways, more difficult) than building traditional software products; how the people involved can impact the outcome; why a good sense of risk management can make all the difference; and what purple cars have to do with all of this. (No, seriously. Purple cars.)Along the way, the pair talk about the early days of the data field, and how much it has changed.
Solomon is active on LinkedIn. You can follow him for his daily updates at https://www.linkedin.com/in/solomonkahnDelivery Layer: https://www.deliverylayer.com/ -
In this episode, James and Q explore:
The ideas of risk and uncertainty.What is "probabilistic thinking" and why is it important for data scientists?The career progression of a data analyst, and what it means to develop statistical acumen.Thinking in terms of distributions, and thinking in different moments of a distribution.Seeing BI, AI, and simulation in terms of punctuation. (No, seriously.)How to bridge the gap into thinking probabilisticallyAnd, just a reminder: James only speaks for himself in this episode and he does not represent his employer.Links mentioned during our discussion:
You can find James on Twitter at @cmastication and on LinkedIn at https://www.linkedin.com/in/jamesdlong/R Cookbook, 2nd Edition (which James co-authored)RenRe's open roles: https://bit.ly/renrejobsQ's blog posts on "punctuation in data": Periods and Question Marks (BI and AI), and then Ellipses (simulation)The list of books James mentioned:
Thinking in Bets (Annie Duke)Fortune's Formula (Poundstone)The Lady Tasting Tea (Salsburg)Fooled by Randomness (Taleb) -
We've all heard the term "economist," sure. But exactly what does and economist do? And as economics is a very data-driven field, where does their work intersect with data science, machine learning, and AI?
To answer that question, Senior Content Advisor Q McCallum spoke with Amar Natt, PhD. She's an economist at Econ One Research, and her work focuses on advanced analytics and predictive modeling. Does that sound like ML to you? Well, Amar explains that it's similar in some ways, different in others. From there, she tells us about techniques economists can learn from data scientists, and what data scientists can pick up from econ. (Hint: "causal inference." You heard it here first.) You can find Amar online:
LinkedIn: https://www.linkedin.com/in/amarita-natt-ph-d-79028313/Econ One Research: https://www.econone.com/staff-member/amarita-natt/Be part of the conversation and connect with the data science community at DSS Miami Hybrid on September 21, 2022.
Book your ticket now.
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When people think about The Home Depot, they probably think more about lumber
LinkedIn: https://www.linkedin.com/in/patwoowong/"How THD keeps shelves stocked using ML" (the talk he mentioned during our interview): https://twimlai.com/podcast/twimlai/how-ml-keeps-shelves-stocked-home-depot-pat-woowong/"The Value Proposition for Using ML in Brick-and-Mortar Retail Stores: Home Depot" https://www.youtube.com/watch?v=rF8jtdX-hGo
and tile than they do ML models. Sure, there is plenty of lumber. But machine learning also plays a key role in the business, in places that customers can see as well as the behind-the-scenes operations.Senior Content Advisor Q McCallum met up with Pat Woowong, Director of Data Science at The Home Depot, to explore how the company mixes their very rich dataset with domain knowledge to employ machine learning deep inside the business. To frame this, he walked me through the Falloff model and Lead scoring, two projects that his team deployed to address the unique challenges of a company that handles both retail and services.During our conversation, we discussed: understanding where models fit into the bigger business picture; using expert domain knowledge to drive feature selection and feature engineering; the value of process; and, to top it off, what it's like to work at The Home Depot.Other places to find Pat:Be part of the conversation and connect with the data science community at DSS Miami Hybrid on September 21, 2022.
Book your ticket now.
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This episode is a coffee chat recording from DSS Virtual in May 2022. Charles Irizarry (Phygital) and Ankita Mangal (P&G) share in war stories of ML use cases they use in retail and eCommerce scenarios, brokering data, and protecting the important principles of data ethics and privacy. Ankita shares the digital transformation journey that P&G undertook, her growth together with P&G, and some of the incredible technologies P&G has developed to better serve their customers world wide.
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A lot of data scientists work in the private sector: finance, adtech, retail, and all that. Today's guest offers her perspective on what it means to do data work in the federal space.In this conversation, our Senior Content Advisor Q McCallum spoke with Dr. Pragyansmita Nayak, Chief Data Scientist at Hitachi Vantara Federal. They explored how different federal agencies use data and how they share datasets with each other. They also talked about how to measure operational efficiency, when you can't rely on metrics like "profit." And, the big question: should we release t-shirts that read "just give me my AI solution!" ?You can find Pragyan online:
Twitter: https://twitter.com/SorishaPragyanLinkedIn: http://linkedin.com/in/pragyansmitaThe book Q mentioned is Army of None, by Paul Scharre.
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In this episode, our Senior Content Advisor Q McCallum met up with Murium Iqbal from Etsy. They spoke about an important skill for data scientists: software development!
Data scientists write a lot of code, sure, but few of them come from a formal software dev background. That can lead them to struggle with slow, buggy code that ultimately holds back the company's ML efforts. Want to write cleaner, more performant code? Looking for ways to make those model deployments more reproducible? Listen to Murium and Q explore topics such as writing tests, using Docker to isolate dependencies, and learning best practices from your software developer teammates. -
This episode is a recording of the panel conversation at the virtual Data Science Salon in April 2022, which focused on AI & machine learning applications in the enterprise.
Charles Irizarry (CEO & Co-Founder at Strata.ai) had the chance to talk to Amarita Natt (Managing Director, Data Science at Econ One Research), Preethi Raghavan (VP, Data Science Practice Lead at Fidelity Investments) and Serg Masís (Climate and Agronomic Data Scientist at Syngenta) about the important topic of model interpretability and how to create trust in AI products. -
Charles Irizarry, CEO & Co-Founder at Strata.ai had the chance to talk to Nirmal Budhathoki, Senior Data Scientist at VMware Carbon Black and Moody Hadi, Group Manager - New Product Development & Financial Engineering at S&P Global. Tune in to hear about ML techniques they are using in their current roles, tools to put ML into production, model explainability, and future trends.
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In the previous episode, our Senior Content Advisor Q McCallum met with product manager Chris Butler to explore the role of uncertainty and how it relates to AI product management. That conversation sets the stage for Chris and Q to talk about communal computing today.
Chris starts by explaining what shared, AI-backed devices mean for data collection, analysis, and regulation. After that, Chris and Q explore important questions such as: What are some challenges in getting communal computing devices to coordinate? How do social norms mix with assumptions made by the ML models behind these devices? What do we lose when we use data lakes? How do product managers and machine learning engineers interact on these kinds of projects? What do communal computing devices have in common with software developers on shared platforms?And, most importantly: what does all of this have to do with the film Napoleon Dynamite ...?
Chris has published a series of articles on communal computing: Communal Computing intro, Communal Computing’s Many Problems, and A Way Forward with Communal Computing.You can also watch some of Chris’s communal computing talks:AIxDesign Communal Computing workshop with animistic design mappingBots and AI Meetup - Communal Computing - Solving multi-user Alexa and Google Assistant use cases - Mostrar mais
