High Signal: Data Science | Career | AI - Episodes Tagged with “Machine Learning”

Episode 23: Why Most AI Agents Fail (and What It Takes to Reach Production)

Delphina — Tue, 02 Sep 2025 06:00:00 -0400

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 publishing to finance rethink their workflows.

We dig into how Atlassian’s culture enables bottom-up experimentation, why grounding and reliability are critical for adoption, and how non-technical teams are often the ones creating the most useful agents. The conversation also looks ahead to the frontiers of multiplayer agent collaboration, proactive and ambient workflows, and the governance and compliance challenges enterprises will face as agents move from tools to teammates.

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Episode 18: High-Stakes AI Systems and the Cost of Getting It Wrong

Delphina — Thu, 19 Jun 2025 05:15:00 -0400

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

Delphina — Thu, 29 May 2025 11:00:00 -0400

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

Delphina — Tue, 13 May 2025 02:00:00 -0400

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

Delphina — Thu, 24 Apr 2025 01:00:00 -0400

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, 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)

Delphina — Wed, 09 Apr 2025 23:00:00 -0400

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

Delphina — Thu, 27 Mar 2025 01:00:00 -0400

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

Delphina — Thu, 13 Mar 2025 15:00:00 -0400

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 10: AI Won't Save You But Data Intelligence Will

Delphina — Wed, 12 Feb 2025 18:15:00 -0500

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

Episode 9: Why 90% of Data Science Fails—And How to Fix It -- With Eric Colson

Delphina — Thu, 30 Jan 2025 15:00:00 -0500

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/

SHOW NOTES

Episode 8: From Zero to Scale: Lessons from Airbnb and Beyond

Delphina — Thu, 09 Jan 2025 00:00:00 -0500

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/

SHOW NOTES

Episode 7: What Lies Beyond Machine Learning and AI: Decision Systems and the Future of Data Teams

Delphina — Wed, 18 Dec 2024 22:00:00 -0500

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.

Key topics from the conversation include:
• From Prediction to Prescription: Why organizations need to focus on interventions that drive outcomes, illustrated with insights like, “Imagine a hospital prescribing treatments instead of just diagnosing conditions.”
• The AI Hierarchy of Needs: Foundational practices, such as data logging and engineering, that enable advanced machine learning and AI.
• Personalization and Optimization: How reinforcement learning and exploration-exploitation methods help optimize KPIs and adapt to user context.
• Scaling Data Teams: Strategies for attracting and retaining talent by emphasizing autonomy, mastery, and purpose.
• Empathy as a Data Science Skill: The importance of collaborating with other teams and understanding their goals to drive adoption and success.

🎧 Tune in to learn how to build decision systems, integrate causality into workflows, and develop scalable data science teams for real-world impact.

You can find more on our website: https://high-signal.delphina.ai/

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Episode 6: What Happens to Data Science in the Age of AI?

Delphina — Wed, 04 Dec 2024 13:00:00 -0500

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 discussion include:

Judgment as a Competitive Edge: Hilary emphasizes the enduring importance of human judgment in framing problems and evaluating AI outputs.
The Future of Generative AI: She discusses its transformative potential while cautioning against over-reliance on prompts, advocating for systems rooted in rich context.
Building for Creativity with Hidden Door: Hilary shares how her company turns generative AI’s liabilities into assets, creating immersive, bias-aware storytelling experiences.
The Shifting Role of Data Science Careers: With automation redefining entry-level roles, Hilary outlines how data professionals can focus on transferable skills to stay ahead.
Navigating AI Strategy in Leadership: She offers pragmatic advice on balancing the hype of AI with practical business impact, aligning leadership expectations with achievable goals.

The conversation concludes with Hilary’s optimistic take on how the data science community can continue to thrive by embracing creativity, empathy, and interdisciplinary collaboration.

🎧 Tune in to gain practical insights into building robust AI systems, navigating career shifts, and leveraging generative AI for meaningful innovation.

You can find more on our website: https://high-signal.delphina.ai/

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Episode 5: The Hard Truth About Building AI Systems and What Most Leaders Miss About AI

Delphina — Wed, 20 Nov 2024 16:00:00 -0500

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.

Highlights from the discussion include:

Bridging the C-Level and Technical Divide: Gabriel emphasizes the importance of aligning leadership with on-the-ground teams to build effective, data-driven organizations.
Starting with the Basics: From building pipelines to identifying high-ROI projects, Gabriel outlines foundational steps for companies adopting data science and AI.
Cultural Transformation for Experimentation: He explains why fostering an experimentation culture, where negative results are valued for learning, is essential for success.
Opportunities in Latin America: Gabriel shares insights on the unique challenges and immense potential of the Latin American tech ecosystem, including the critical role of startups and the need for local innovation systems.
Generative AI’s Role in Driving Impact: Discussing generative AI’s transformative potential, Gabriel highlights its capacity to lower barriers for smaller teams while emphasizing the importance of problem-first approaches.

The conversation concludes with a forward-looking exploration of opportunities in government, education, and healthcare, and Gabriel’s optimism about building ecosystems where startups and local talent thrive.

🎧 Tune in to learn from Gabriel’s thoughtful perspectives on navigating the complexities of building data-driven cultures, the global AI landscape, and how to leverage data for impactful change.

You can find more on our website: https://high-signal.delphina.ai/