High Signal: Data Science | Career | AI
Episode Archive
Episode Archive
18 episodes of High Signal: Data Science | Career | AI since the first episode, which aired on October 19th, 2024.
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Episode 18: High-Stakes AI Systems and the Cost of Getting It Wrong
June 19th, 2025 | 58 mins 45 secs
ai, data science, llms, machine learning
Sudarshan Seshadri—VP of AI, Data Science, and Foundations Engineering at Alto Pharmacy—joins us to explore what it takes to build high-stakes AI systems that people can actually trust. He shares lessons from deploying machine learning and LLMs in healthcare, where speed, safety, and uncertainty must be carefully balanced. We talk about designing AI to support pharmacist judgment, the shift from bottlenecks to decision backbones, and why great data leaders are really architects of how irreversible decisions get made.
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Episode 17: The Incentive Problem in Shipping AI Products — and How to Change It
May 29th, 2025 | 53 mins 52 secs
ai, data science, machine learning
Roberto Medri, VP of Data Science at Instagram, explains why most experiments fail, how misaligned incentives warp product development, and what it takes to drive real impact with data science. He shares what teams get wrong about launches, why ego gets in the way of learning, and how Instagram turned Reels from a struggling product into a global success. A candid look at product, data, and decision-making inside one of the world’s most influential platforms.
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Episode 16: How Human-Centered AI Actually Gets Built
May 13th, 2025 | 47 mins 22 secs
data science, genai, llms, machine learning
Fei-Fei Li—co-director of Stanford’s Human-Centered AI Institute and one of the most respected voices in the field—reflects on AI’s evolution from the early days of ImageNet to the rise of foundation models. She explains why spatial intelligence may be the next major shift, how human-centered design applies in practice, and why AI should be understood as a civilizational technology—one that shapes individuals, communities, and society at large.
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Episode 15: Why Good Metrics Still Lead to Bad Decisions — and How to Fix It
April 24th, 2025 | 54 mins 17 secs
data science, genai, llms, machine learning
Eoin O'Mahony—data science partner at Lightspeed, former Uber science lead, and co-designer of the system that kept NYC’s Citi Bikes available across the city—argues that positive metrics are meaningless if you don’t understand the mechanism behind them. At Uber, he was careful to make sure his launches both looked good on paper and made sense in practice. Now in venture, he’s applying that same rigor to unstructured data—using GenAI to scale a kind of work that’s long resisted systematization.
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Episode 14: Why Most Companies Aren’t Actually AI Ready (and What to Do About It)
April 9th, 2025 | 51 mins 58 secs
data science, genai, llms, machine learning
Barr Moses—co-founder and CEO of Monte Carlo—thinks we’re headed for an AI reckoning. Companies are building fast, but most are still managing data like it’s 2015. In this episode, she shares high-stakes failure stories (like a $100M schema change), explains why full-stack observability is becoming essential, and breaks down how LLM agents are already transforming data debugging. From culture to tooling, this is a sharp look at what real AI readiness requires—and why so few teams have it.
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Episode 13: The End of Programming As We Know It
March 27th, 2025 | 1 hr 23 mins
ai, data science, genai, llms, machine learning
Tim O’Reilly—founder of O’Reilly Media and one of the most influential voices in tech—argues we’re not witnessing the end of programming, but the beginning of something far bigger. He draws on past computing revolutions to explore how AI is reshaping what it means to build software, why real breakthroughs come from the edge—not incumbents—and what it takes to learn, teach, and build responsibly in the age of AI.
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Episode 12: Your Machine Learning Solves The Wrong Problem
March 13th, 2025 | 54 mins 40 secs
causal inference, causal ml, data science, machine learning
Stefan Wager—Professor at Stanford and expert on causal machine learning—has worked with leading tech companies including Dropbox, Facebook, Google, and Uber. He challenges the widespread assumption that better predictions mean better decisions. Traditional machine learning excels at prediction, but is prediction really what your business needs? Stefan explores why predictive models alone often fail to answer critical “what-if” questions, how causal machine learning bridges this gap, and provides practical advice for how you can start applying causal ML at work.
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Episode 11: What Comes After Code? The Role of Engineers in an AI-Driven Future
February 27th, 2025 | 1 hr 5 mins
Peter Wang—Chief AI Officer at Anaconda and a driving force behind PyData—challenges conventional thinking about AI’s role in software development. As AI reshapes engineering, are we moving beyond writing code to orchestrating intelligence? Peter explores why companies are fixated on models instead of integration, how AI is breaking traditional software workflows, and what this shift means for open source. He also shares insights on the evolving role of engineers, the commoditization of AI models, and the deeper questions we should be asking about the future of software.
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Episode 10: AI Won't Save You But Data Intelligence Will
February 12th, 2025 | 59 mins 42 secs
ai, data science, machine learning
Ari Kaplan—Global Head of Evangelism at Databricks and a pioneer in sports analytics—explains why businesses fixated on AI often overlook the real advantage: making better decisions with their own data. He shares lessons from his work building analytics teams for Major League Baseball, advising McLaren’s F1 strategy, and helping companies apply AI where it actually works—without falling into hype-driven traps.
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Episode 9: Why 90% of Data Science Fails—And How to Fix It -- With Eric Colson
January 30th, 2025 | 1 hr 9 mins
ai, data science, machine learning
Eric Colson—former Chief Algorithms Officer at Stitch Fix and VP of Data Science and Machine Learning at Netflix—explains why most companies fail to fully leverage their data science teams. Drawing on his experience leading data functions at top tech companies, he shares how organizations can move beyond treating data science as a support function and instead empower data scientists to drive strategic impact through experimentation, iteration, and algorithmic decision-making.
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Episode 8: From Zero to Scale: Lessons from Airbnb and Beyond
January 9th, 2025 | 1 hr 6 mins
ai, data science, machine learning
Elena Grewal—former Head of Data Science at Airbnb, political consultant, professor at Yale, and ice cream shop owner—shares her journey of building data teams that scale across vastly different contexts. Drawing on her experiences in tech, consulting, and brick-and-mortar, Elena offers practical lessons on leadership, trust, and experimentation.
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Episode 7: What Lies Beyond Machine Learning and AI: Decision Systems and the Future of Data Teams
December 18th, 2024 | 1 hr 18 mins
ai, data science, genai, llms, machine learning
In this episode of High Signal, Chris Wiggins—Chief Data Scientist at The New York Times, Professor at Columbia University, and co-author of How Data Happened—shares how organizations can move beyond prediction to actionable decision systems. Drawing on his work at The New York Times and in academia, Chris explains how to scale data teams, optimize systems, and align data science with organizational impact.
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Episode 6: What Happens to Data Science in the Age of AI?
December 4th, 2024 | 1 hr 18 mins
data science, genai, llms, machine learning, nlp, prompt engineering
In this episode of High Signal, Hilary Mason—renowned data scientist, entrepreneur, and co-founder of Hidden Door—shares her unique insights into the evolving world of data science and generative AI. Drawing from her pioneering work at Fast Forward Labs, Bitly, and Hidden Door, Hilary explores how creativity, judgment, and empathy are reshaping the data landscape.
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Episode 5: The Hard Truth About Building AI Systems and What Most Leaders Miss About AI
November 20th, 2024 | 1 hr 2 mins
ai, data science, machine learning
In this episode of High Signal, Gabriel Weintraub (the Amman Professor of Operations, Information, and Technology at Stanford Graduate School of Business), brings his expertise in market design, data science, and operations, enriched by his experience with global platforms like Uber and Mercado Libre, to a conversation that spans practical strategies, cultural insights, and global perspectives on data and AI.
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Episode 4: How to Build an Experimentation Machine and Where Most Go Wrong
November 7th, 2024 | 51 mins 16 secs
Ramesh Johari (Stanford, Uber, Airbnb, and more) explores the art and science of online experimentation, especially in the context of marketplaces and tech companies.
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Episode 3: Data Science Meets Management: Teamwork, Experimentation, and Decision-Making
October 19th, 2024 | 52 mins 12 secs
Chiara Farronato (Harvard Business School) discusses how digital platforms like Airbnb and Uber have transformed industries. She explores the challenges of fostering collaboration between managers and data scientists, bridging communication gaps, and building data-driven cultures. Chiara also delves into the complexities of managing peer-to-peer marketplaces and the evolving role of data in decision-making. This episode offers key insights for business leaders working with technical teams and navigating platform-based innovation.