High Signal: Data Science | Career | AI

• 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 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. LINKS Stefan's Stanford Website (https://www.gsb.stanford.edu/faculty-research/faculty/stefan-wager) Machine Learning and Economics, Stefan and Susan Athey's lectures for the Stanford Graduate School of Business (https://www.youtube.com/@stanfordgsb) Causal Inference: A Statistical Learning Approach (WIP!) (https://web.stanford.edu/~swager/causal_inf_book.pdf) Mastering ‘Metrics: The Path from Cause to Effect by Angrist & Pischke (https://www.masteringmetrics.com/) The Book of Why: The New Science of Cause and Effect by Judea Pearl and Dana Mackenzie (https://en.wikipedia.org/wiki/The_Book_of_Why) Causal Inference: The Mixtape by Scott Cunningham (https://mixtape.scunning.com/) A Technical Primer On Causality by Adam Kelleher (https://medium.com/@akelleh/a-technical-primer-on-causality-181db2575e41) What Is Causal Inference? An Introduction for Data Scientists by Hugo Bowne-Anderson and Mike Loukides (https://www.oreilly.com/radar/what-is-causal-inference/) The Episode on YouTube (https://www.youtube.com/watch?v=f9_Lt5p8avU&feature=youtu.be) Delphina's Newsletter (https://delphinaai.substack.com/)

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• 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 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. LINKS Peter Wang on LinkedIn (https://www.linkedin.com/in/pzwang/) Anaconda (https://www.anaconda.com/) Mistral Saba (https://mistral.ai/news/mistral-saba) Peter chatting with Hugo several years ago about the beginnings of PyData, NUMFOCUS, and Python for Data Science (https://vanishinggradients.fireside.fm/7)

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• 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 into hype-driven traps. SHOW NOTES Ari on LinkedIn (https://www.linkedin.com/in/arikaplan/) The Data Intelligence Platform For Dummies by Ari and Stephanie Diamond (https://www.databricks.com/resources/ebook/maximize-your-organizations-potential-data-and-ai) Databricks' AI/BI: Intelligent analytics for real-world data (https://www.databricks.com/product/ai-bi) That time Ari spoke with Travis Kelce  about how Travis and the Kansas City Chiefs use data and analytics! (https://www.linkedin.com/posts/arikaplan_wiley-databricks-genai-activity-7221214362575724545-RZwc/)

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• 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 a service function to empowering them as strategic drivers of business impact. Key topics from the conversation include: Data Science as a Strategic Function: Why many companies limit their data teams to answering business requests instead of leveraging their ideas for competitive advantage. Beyond Skills—The Power of Cognitive Repertoires: How data scientists' unique ways of framing problems can lead to breakthrough innovations. Trial and Error as a Competitive Advantage: Why most experiments fail—but scaling experimentation is the key to big wins. Decoupling Algorithms from Applications: How separating data science from engineering enables rapid iteration and direct business impact. Shifting from Cost Center to Revenue Generator: Practical steps for structuring data teams to drive measurable value and long-term success. 💡 Tune in to learn how leading companies structure their data teams for impact, why experimentation beats rigid planning, and how treating data science as a strategic function can unlock new business opportunities. You can find more on our website: https://high-signal.delphina.ai/ (https://high-signal.delphina.ai/) SHOW NOTES Eric on LinkedIn (https://www.linkedin.com/in/ecolson/) Beyond Skills: Unlocking the Full Potential of Data Scientists by Eric Colson (https://www.oreilly.com/radar/beyond-skills-unlocking-the-full-potential-of-data-scientists/) MultiThreaded: Technology at StitchFix (https://multithreaded.stitchfix.com/) A/B Testing with Fat Tails by Azevedo et al. (https://eduardomazevedo.github.io/papers/azevedo-et-al-ab.pdf)

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• 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 topics from the conversation include: From Zero to Scale: How Elena built Airbnb’s data science function from the ground up, scaling it to a 200-person team while driving impact across the organization. Trust and Team Culture: Why trust is foundational for building effective teams, fostering creativity, and empowering data scientists to drive results. Applying Data Science Across Contexts: Lessons learned from using data to inform decisions in politics, academia, and even running an ice cream shop. Experimentation and Iteration: Insights into tailoring experimentation methods to fit different scales, from small businesses to tech giants. Critical Thinking and Data: How Elena equips the next generation of leaders at Yale to ask better questions, assess data quality, and think critically about evidence. 💡 Tune in to explore how data science principles can scale across industries, the leadership skills required to build impactful teams, and why experimentation is as relevant to ice cream as it is to AI systems. You can find more on our website: https://high-signal.delphina.ai/ (https://high-signal.delphina.ai/) SHOW NOTES Elena's website (https://www.elenagrewal.com/) Elena on LinkedIn (https://www.linkedin.com/in/elena-grewal) Real World Environmental Data Science, Elena's course at Yale (https://resources.environment.yale.edu/courses/detail/617?_gl=1*afq82v*_ga*MTcxODQ0NjM2Mi4xNzM2NDA1MzI1*_ga_THKV4HP9QY*MTczNjQwNTMyNC4xLjAuMTczNjQwNTMyNC4wLjAuMA..*_ga_G9Q7CGGC6Y*MTczNjQwNTMyNC4xLjAuMTczNjQwNTMyNC4wLjAuMA..) Elena's on Orange! (https://www.elenasonorange.com/)

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