The Test Set by Posit

This episode is Part 2 of our conversation with James Blair. He explains how he found his “accidental perfect fit” as a solutions engineer and how that role became a pipeline into product management. Get a peek into the AI-powered tooling he’s now building for the Posit ecosystem, and hear how he’s using Claude Code, Positron Assistant, and DataBot to generate synthetic, industry-specific demos on the fly — plus, why the real magic is keeping humans firmly in the loop. Episode notesThis is a story about listening deeply to users and using AI to make that listening scale. James explains what solutions engineers actually do, how that work shaped Posit’s product team, and how synthetic data plus agents are changing the way they build demos and teach data science. What’s insideWhat a solutions engineer really is and why the role was such a good fit for JamesHow solutions engineering became a natural pathway into product management at PositMulti-agent “bot posse” workflows and why context management mattersUsing AI the right way and why code literacy, critical thinking, and staying human are the real superpowers in an AI-saturated world

In Part 1 of our conversation with James Blair, we trace his delightfully non-linear path from childhood robotics dreams to journalism to R, with a few stops in between. We hear about the Shiny app that changed his career, plus a candid roundtable with Michael, Hadley, and Wes about whether a data-science master’s still pays off in the age of AI.Episode notesThis is a story about staying hands-on and fiercely inquisitive — whether analyzing bike telemetry or in teaching data science. James shares how early experimentation with Shiny helped shape his career, and how curiosity (not credentials) still powers meaningful work in data science.What’s insideA winding path from robotics to journalism to psychology to data scienceDiscovering the power of applied statsThe value (and limits) of a data-science master’s in a shifting AI landscapeFighting confirmation bias: good analysis resists the answer you want

In part two of our conversation with Julia Silge, we discuss how work actually ships: the boundaries, the glue, and the tools that turn noise into signal. From there, we go macro and wonder what the LLM era means for humanity’s contributions, plus how licensing is evolving to protect sustainability without abandoning openness.Episode notesBoth practical and philosophical, this conversation spans workplace energy, team connective tissue, and the big questions LLMs have us asking in a shifting data science landscape.What’s insideJulia’s system for turning scattered community signals (GitHub, Stack Overflow, discourse) into product insightThe power of “glue” work, and where to find the winsFrom Stack Overflow to LLMs: What changed when communal Q&A became model fuel — and what that means for finding answersLicenses in a new era: Threading the needle between MIT-style generosity and elastic-style sustainability for platformed softwareTry Positron: Where to download, read docs, and give feedback

In part one of our conversation with Julia Silge, astronomer-turned–data-science leader, we explore why data science needs a different kind of IDE. Julia takes us inside Positron, Posit’s next-generation, data-scientist-first environment, and unpacks the day-to-day realities that make data science work unlike software engineering. Along the way, we get a first-hand account of a legendary pineapple-pizza protest and how to juggle multiple projects at once.Episode Notes:A behind-the-scenes tour of Positron and the workflows it’s built for, plus the stories, trade-offs, and team choreography required to ship an IDE on a living substrate. We talk extension ecosystems, upstream merges, data viewers, and more. Plus, Julia shares why applied systems (and messy, real-world data) are her happy place.What’s Inside:The pineapple-pizza story that unexpectedly went viral — and what “context collapse” feels like from the insideWhy Positron is a data-science-first IDE, optimized for analysis, not general software engineeringIteration vs. reproducibility: the central tension in data science workflows and how tooling can honor bothHadley’s cold-turkey move from RStudio, muscle memory, and finding the new ergonomic grooveHow Julia measures success by smoothing the boundaries between tools and teamsThe applied, people-and-process side of data science that keeps Julia energized

For this episode, we turn the mic around. Wes McKinney takes over the interviewer’s chair to chat with his co-host, Michael Chow. Michael’s a principal software engineer at Posit, but he started out studying how people think — literally, with a PhD in cognitive psychology. Somewhere along the way, he got hooked on data science, helped build adaptive learning tools at DataCamp, and now spends his days thinking about how to make Python easier to use and more fun.The two dig into what drives Michael’s curiosity, how a “weird obsession with tables” turned into a beloved open source project, and the future of data science/scientists.Episode Notes:We explore Michael’s path from studying the mind to shaping the Python data science ecosystem. From adaptive learning platforms to Great Tables, Michael shares how following unexpected curiosities can spark tools and communities that last.What’s Inside:Michael’s pivot from an academic career in data scienceBehind-the-scenes messiness of building data and learning platformsOpen source projects born out of zany, single-minded passionsBringing beauty to rows and columnsBig-picture thoughts on where data science — and open source tooling — are headed

Roger Peng, a Professor of Statistics and Data Science at UT Austin and co-host of Not So Standard Deviations, discusses his unique journey in data science. He shares insights from his early projects that shaped his passion for R and the importance of hands-on experience in education. The conversation dives into the dynamics of podcasting and how to maintain meaningful content over time. Roger also emphasizes the evolving roles of programming languages like R and SQL, and the community's pivotal role in shaping the landscape of data science.

Mine Çetinkaya-Rundel, a data science educator at Duke University and Posit, shares her compelling journey from actuarial science to the classroom. She discusses her innovative teaching philosophy, the 'whole game' approach that keeps students engaged. The conversation dives into her use of AI, specifically LLMs, for instant feedback on assignments. Mine also addresses the importance of manual coding skills in the age of AI and reflects on the unique relationship between the R and Python communities in fostering collaboration and open-source learning.

In the latest discussion, Wes McKinney, an open source software developer and creator of pandas, delves into the intricacies of maintaining open source projects. He explains the essential role of corporate support and shares the origin story of Apache Arrow. The conversation highlights the development of Positron, a cutting-edge IDE, and the importance of interoperable tools across programming languages. Wes also touches on how modern IDEs should cater to both users and machine learning models, blending his coding insights with a passion for music.

Wes McKinney, an influential open-source software developer and the mastermind behind Pandas and Apache Arrow, shares his journey from speedrunning communities to revolutionizing data processing. He discusses how frustration with data work sparked Pandas' creation and dives into the early days of organizing the GoldenEye speedrunning scene. Wes also reflects on the importance of community in open-source development, the challenges of optimizing CSV handling, and the thrill of collaborating with like-minded creators in the data space.

Hadley Wickham, Chief Scientist at Posit and creator of the Tidyverse, shares his journey from a spreadsheet-loving teen to a major figure in data science. He reflects on how messy Excel sheets led to the tidy data revolution and discusses the challenges of developing R packages. The conversation touches on maintaining focus amidst burnout, his fascination with integrating LLMs into data workflows, and how writing books helps tidy ideas. Fun anecdotes, including a custom bike inspired by his Shiny textbook, add a personal touch to his insights.