High Signal: Data Science | Career | AI
High Signal is a podcast for data science, AI, and machine learning professionals. Hosted by Hugo Bowne-Anderson and produced by Delphina, each episode features deep conversations with leading experts such as Michael Jordan, Andrew Gelman, and Chiara Farranato. The show provides practical insights to help listeners advance their careers and make an impact in these rapidly evolving fields.
Episodes
Episode 41: The Verification Crisis: Why Trust Is the New Bottleneck in AI
Noah Smith, economist and author of Noahpinion, joins High Signal to look at what AI is already changing… and what it isn’t. The conversation moves beyond the usual productivity hype to ask harder questions: Is agentic coding actually increasing revenue per hour worked? Will software remain a high-margin business if AI makes it easy to clone? And what happens when generating content, code
Episode 40: The Economic Reality of AI: Friction, Talent, and the Future of the Firm
Steve Tadelis, Professor of Economics at UC Berkeley and former senior economist at eBay and Amazon, joins High Signal to bridge the gap between economic theory and the high-stakes reality of data science and AI. Drawing on his experience at the forefront of the world’s largest marketplaces, Steve discusses the "invisible friction" that prevents organizations from acting on data: a combin
Episode 39: The 100-Year Lead: What Baseball Teaches Us About the Future of AI
Chris Fonnesbeck, veteran analyst for the Yankees and Mets and creator of the open-source Bayesian modeling library PyMC, joins to unpack why baseball has been a leading indicator for data science and analytics for over a century, and why builders and AI leaders need to pay attention now. The reason it has led is simple: huge incentives and a culture that treats decisions as quantifiable.
Episode 38: Why AI Won’t Fix Your Data Culture, It Will Only Amplify It (And What To Do About It)
Noah Bruegmann, President of Data CRT, joins High Signal to discuss how to move your data function from a cost center to a strategic "value center". He explains how AI amplifies your existing data culture, the importance of "no-assistance" reporting, and how rebranding documentation as "Context" can finally secure executive buy-in. Drawing on 15 years of experience spanning trading floors
Episode 37: Engineered Intelligence and The Data Science Problem in AI
Jordan Morrow, SVP of Data & AI Transformation at AgileOne and the "Godfather of Data Literacy," joins High Signal to discuss the shift from being "data-driven" to becoming "AI-enabled." Jordan warns that many organizations are walking into the same traps that derailed the data science era a decade ago: prioritizing expensive tooling and hype over the cultural change and literacy requ
Episode 36: AI and the Judgment Problem in Data Science
Dawn Woodard (Distinguished Engineer, LinkedIn), Andrés Bucchi (LATAM Airlines), and Jeremy Hermann (CEO & Co-Founder, Delphina) join High Signal for a deep dive into the shifting architecture of data science & analytics in the era of AI. As the industry moves from static dashboards to vibe coding and conversational querying, this panel of industry veterans explores why traditiona
Episode 35: Beyond Online Experimentation: Generative Software That Optimizes Itself
Martin Tingley, Head of Windows Experimentation at Microsoft and former Head of the Experimentation Platform Analysis Team at Netflix, talks about why humans are the bottleneck in experimentation, and how a five-level maturity framework points the way toward self-optimizing software.
Our conversation traces the path from basic hypothesis testing to a frontier where Generative AI creates,
Episode 34: Duolingo and the Future of Personalized Education with AI
Bozena Pajak, VP of Learning at Duolingo, joins High Signal to discuss the evolution of AI at Duolingo: from personalized difficulty models to the current generative frontier where AI characters provide low-stakes and high impact conversational practice. We discuss the role of AI in overcoming one of the biggest hurdles in language acquisition, speaking anxiety. We also talk about how Boz
Episode 33: Why Your AI Product Will Be Obsolete in Six Months (And What To Do About It)
Benn Stancil, writer and co-founder of Mode, joins High Signal to ask some uncomfortable questions about the current AI moment. Is now actually a terrible time to start a company? If the tools you build on today are obsolete in six months, at what point does the head start stop mattering? Is all that context engineering you're doing a waste of time, destined to go the way of Boolean searc
Episode 32: The Post-Coding Era: What Happens When AI Writes the System?
Nicholas Moy, former Head of Research at Windsurf & now at Google DeepMind, joins High Signal to discuss the shift from "co-driving" to a truly "agentic" era of development. We discuss Windsurf's journey from early prototypes that struggled with compounding errors to the successful launch of their agentic coding product. Nick explains that building a startup in the current climate re
Episode 31: Why Data Governance In Your Org is Broken (And How to Fix It)
Cara Dailey, VP and Head of Data Strategy at Early Warning (the parent company of Zelle), joins High Signal to discuss the evolution of high-stakes data leadership and governance. From her early work in online advertising at DoubleClick to shaping data strategy at Nike and holding Chief Data Officer roles at Bank of the West and T. Rowe Price, Cara has seen every iteration of the data lea
Episode 30: The AI Paradox: Why Your Data Team’s Workload is About to Explode
Chris Child, VP of Product, Data Engineering at Snowflake, joins High Signal to deliver a new playbook for data leaders based on his recent MIT report, revealing why AI is paradoxically creating more work for data teams, not less. He explains how the function is undergoing a forced evolution from back-office “plumbing” to the strategic core of the enterprise, determining whether AI initia
Episode 29: Why AI Adoption Fails: A Behavioral Framework for AI Implementation
Liz Costa of the Behavioral Insights Team returns to High Signal to deliver a critical behavioral science playbook for the AI era focused on human and business impact. We discuss why the potential of AI can only be fulfilled by understanding a single bottleneck: human behavior. The conversation reveals why leaders must intervene now to prevent temporary adoption patterns from calcifying i
Episode 28: From Context Engineering to AI Agent Harnesses: The New Software Discipline
Lance Martin of LangChain joins High Signal to outline a new playbook for engineering in the AI era, where the ground is constantly shifting under the feet of builders. He explains how the exponential improvement of foundation models is forcing a complete rethink of how software is built, revealing why top products from Claude Code to Manus are in a constant state of re-architecture simpl
Episode 27: Why Your Data Team Doesn't Have a Seat at the Table (And How to Earn It)
Paras Doshi (Head of Data, Opendoor; former data leader at Amazon) joins High Signal to unpack the playbook for building an indispensable data function. He shares his experience tackling the classic scaling challenge of fragmented data at Opendoor, where rapid growth led to inconsistent metrics across the business, and turning the data function into a centralized strategic asset.
We dive
Episode 26: Gen AI's True Cost: Why Today's Wins Are Tomorrow's Debts
Vishnu Ram Venkataraman (Generative AI Executive & Entrepreneur; former AI Leader at Credit Karma and Intuit) joins High Signal to unpack the true cost of generative AI. Having scaled AI solutions impacting over 140 million users, Vishnu reveals why the ease of shipping Gen AI prototypes often masks significant operational and engineering debts, challenging the conventional wisdom of
Episode 25: How Data-Driven Growth Redefined a Media Giant
Sergey Fogelson (VP of Data Science, Televisa Univision) joins High Signal to reveal how the world’s largest Spanish-language media company built a sophisticated data engine from the ground up. This transformation fueled a tenfold expansion of its digital streaming business by redefining how the company connects with 300 million viewers worldwide. At the heart of this success is a proprie
Episode 24: Rebuilding an Airline for the 21st Century: LATAM's Data-Driven Transformation
Andrés Bucchi (Chief Data Officer, LATAM Airlines) joins High Signal to unpack how a century-old airline reinvented itself with data and AI—and how that transformation is unlocking value from fuel efficiency to fraud detection. LATAM has built a massive data operation, experimenting across everything from pricing to operations, while customers benefit from a more reliable and secure trave
Episode 23: Why Most AI Agents Fail (and What It Takes to Reach Production)
Anu Bharadwaj (President, Atlassian) joins High Signal to unpack how humans and AI agents will work together across the enterprise, and how that shift could change the very nature of teamwork. Atlassian employees have already built thousands of agents across product, marketing, engineering, and HR teams, while customers like HarperCollins are cutting manual work by 4x as industries from p
Episode 22: Why a Trillion Dollars of Market Cap Is Up for Grabs (and How AI Teams Will Win It)
Tomasz Tunguz (Theory Ventures) joins High Signal to unpack why a trillion dollars of market cap is up for grabs as AI reshapes enterprise software. He explains why workflows are now changing faster than packaged software can keep up, how “liquid software” is redefining CRM and marketing automation, and why background agents will require a new kind of “agent inbox.” We discuss the compoun
Episode 21: Why Great Data Still Leads to Bad Decisions (And How to Fix It)
Amy Edmondson (Harvard Business School) and Mike Luca (Johns Hopkins) join High Signal to unpack what actually drives good decisions in data‑rich organizations. Using contrasts like the Bay of Pigs vs. the Cuban Missile Crisis and product cases such as Airbnb’s work on measuring discrimination, they show how decision quality tracks conversation quality—framing options, surfacing uncertain
Episode 20: Incentives, Accountability, and the Data Leader’s Dilemma
Daragh Sibley, Chief Algorithms Officer at Literati and former Director of Data Science at Stitch Fix, joins High Signal to unpack how machine-learning moves from slide-deck promise to bottom-line impact. He walks through his shift from academic research on how kids learn to read to owning inventory and personalization algorithms that decide which five books land in every child’s box. We
Episode 19: Defaults, Decisions, and Dynamic Systems: Behavioral Science Meets AI
Lis Costa, Chief of Innovation and Partnerships at the Behavioural Insights Team, joins High Signal to explore how behavioral science is reshaping public policy, digital platforms, and machine learning.
She explains how defaults influence behavior at scale, why personalization and chatbots are unlocking new kinds of interventions, and what happens when AI systems meet real-world complexi
Episode 18: High-Stakes AI Systems and the Cost of Getting It Wrong
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 shi
Episode 17: The Incentive Problem in Shipping AI Products — and How to Change It
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, a
Episode 16: How Human-Centered AI Actually Gets Built
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 th
Episode 15: Why Good Metrics Still Lead to Bad Decisions — and How to Fix It
Eoin O'Mahony—data science partner at Lightspeed, former Uber science lead, and one of the early architects 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,
Episode 14: Why Most Companies Aren’t Actually AI Ready (and What to Do About It)
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 cul
Episode 13: The End of Programming As We Know It
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 respo
Episode 12: Your Machine Learning Solves The Wrong Problem
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
Episode 11: What Comes After Code? The Role of Engineers in an AI-Driven Future
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 mean
Episode 10: AI Won't Save You But Data Intelligence Will
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 i
Episode 9: Why 90% of Data Science Fails—And How to Fix It -- With Eric Colson
In this episode of High Signal, Eric Colson—former Chief Algorithms Officer at Stitch Fix and VP of Data Science and Machine Learning at Netflix—breaks down why most companies fail to unlock the full potential of their data science teams. Drawing from years of experience leading data functions at top tech companies, Eric shares how organizations can shift from treating data scientists as
Episode 8: From Zero to Scale: Lessons from Airbnb and Beyond
In this episode of High Signal, 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.
Key top
Episode 7: What Lies Beyond Machine Learning and AI: Decision Systems and the Future of Data Teams
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 org
Episode 6: What Happens to Data Science in the Age of AI?
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.
Highlights from the dis
Episode 5: The Hard Truth About Building AI Systems and What Most Leaders Miss About AI
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 persp
Episode 4: How to Build an Experimentation Machine and Where Most Go Wrong
Ramesh Johari (Stanford, Uber, Airbnb, and more) explores the art and science of online experimentation, especially in the context of marketplaces and tech companies.
Ramesh shares insights on how organizations evolve from basic experimentation practices to becoming fast, adaptive, and self learning organizations. We dive into challenges like the risk aversion trap, the importance of lea
Episode 3: Data Science Meets Management: Teamwork, Experimentation, and Decision-Making
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
Episode 2: Fooling Yourself Less: The Art of Statistical Thinking in AI
Hugo Bowne-Anderson welcomes Andrew Gelman, professor at Columbia University, to discuss the practical side of statistics and data science. They explore the importance of high-quality data, computational skills, and using simulation to avoid misleading results. Andrew dives into real-world applications like election predictions and highlights causal inference’s critical role in decision-m
Episode 1: The Next Evolution of AI: Markets, Uncertainty, and Engineering Intelligence at Scale
Michael Jordan (UC Berkeley) on the future of machine learning as it extends to a planetary scale in "The Next Evolution of AI: Markets, Uncertainty, and Engineering Intelligence at Scale." In this episode, Mike speaks with Hugo about the evolution of AI, the importance of integrating machine learning, computer science, and economics, and how AI can scale to address planetary-level challe
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