The Test Set by Posit

Nell Thomas, VP of Data at Shopify who has led data teams at Etsy, Facebook, and the DNC, maps the modern data stack and the roles that make it work. She talks data quality, building trust with scrutiny and blameless postmortems. She describes scaling a 400-person org, why semantic layers are so hard, and how AI is changing experiments and metric investigations.

Phillip Cloud, principal engineer at NVIDIA and Ibis lead with roots in early pandas work. He shares how an eye‑movement lab pulled him into open source. Conversations range from terminal user interfaces and VisiData to cold, task‑focused coding agents and their limits. He also riffs on file hierarchy grievances, NixOS praise, and a steadfast stance on pineapple‑on‑pizza.

Charlie Marsh, founder and CEO of Astral and creator of Ruff, UV, and Ty — builds high-performance Python developer tools. He talks about shifting workflows as coding agents write more code. Short takes cover agent-driven review distrust, parallel agent sessions, packaging trade-offs, and whether Python fits an agentic future.

Alenka Frim, a mathematician-turned-yoga teacher who became an Apache Arrow committer and PMC member, tells her unconventional path into core data infrastructure. She talks about discipline from the mat translating to steady open source contribution. Conversations cover Arrow’s role in the data ecosystem, managing thousands of issues, imposter syndrome, and how AI agents are reshaping programming.

Emily Riederer, a data science manager at Capital One and cross-language tool author, talks about her journey through R, Python, and SQL. She dives into messy real-world data, the rise of dbt and better SQL tooling, and why column selectors (yes, really) change ergonomics. She also discusses learning in public, imposter syndrome, and solving boring but high-impact problems.

Rebecca Barter, senior data scientist at Arine and adjunct assistant professor at the University of Utah, refuses to work on things she doesn’t care about. Lucky for us, she cares about a lot, most of all impact. In this episode, Rebecca joins The Test Set to talk about learning fast, building better tools, and staying motivated and adaptable.She shares how moving between R, Python, SQL, and dashboards reshaped how she thinks about expertise. Plus a reflection on her recent posit::conf talk, “AI: Hype, Help, or Hindrance.”Episode NotesRebecca digs into what it’s really like to work with AI every day and why humans still rule, especially in exploratory data analysis. She explains how tool building can be the fastest way out of busywork and how teaching beginners sharpened her ability to communicate clearly. The conversation circles a bigger question too: As AI keeps improving, are we headed toward a future where code looks completely different … or maybe disappears altogether?What’s InsideWhy motivation matters even more than productivityEscaping busywork by building better toolsFrom R to Python to dashboards: Learning fast as a survival skillReality check on AI in the IDEWhy exploratory analysis still needs human intuitionThe 80/20 of coding: Automate the boring, protect the judgmentTeaching beginners and earning trustThe uncertain future of code

You’ve probably used Narwhals without realizing it. It’s the compatibility layer helping apps and libraries like Plotly play nice with Pandas, Polars, Arrow, and more — while keeping computation native instead of converting everything to Pandas. In this episode, Marco Gorelli explains how his weekend experiment turned into essential ecosystem infrastructure and why data types, not APIs, are where interoperability gets tricky. Plus what it takes to build trust and community around an open-source project. Episode NotesMarco shares the Narwhals origin story (including the meme-powered name), the hard edge cases that live in data types and null semantics, and why he’s cautious about using AI for code generation when correctness hinges on tiny details. We also jam on proactive “GitHub surfing,” conference talks as trust-building exercises, celebrating contributors, and how early commit messages capture the genuine excitement of building something new.What’s InsideNarwhals 101: You’ve probably used it (even if you didn’t know it)The real interoperability traps: data types, null semantics, and “looks-the-same” operationsWhy expression systems won, and how they shaped Marco’s approach — with nods to Ibis, Polars, and PandasOpen source as social work: proactive outreach, trust-building, and a Discord-powered communityExtending Narwhals to new engines, starting with the Daft plugin

In this episode, we’re joined by Kelly Bodwin — candy corn defender, board game enthusiast, and Associate Professor of Statistics and Data Science at Cal Poly. We discuss her path from English and French to statistics, how she builds teaching tools and navigates AI in the classroom, and what it takes to keep a programming community weird in the best possible way.Episode notesKelly is curious, collaborative, and unafraid to lean in on quirky. Kelly shares how she balances teaching three courses with master's student supervision, applied research projects spanning Polish history and beyond, and her belief that the best part of academia is the people. We also dive into the practical and philosophical challenges of staying current in a field that reinvents itself every few years.What's insideBreakfast mixologyBuilding Quarto extensions with JavaScript and AIWhen ChatGPT helps students learn (and when it doesn't)Applied stats meets history: analyzing social networks from the Polish RevolutionWhy remarkable, welcoming communities matter more than perfect code

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