Rebecca Barter: Persistent learning, tool building, and ‘Will code even exist?’ @PositPBC

Uploaded January 2026 | Updated September 2026, 3 weeks ago

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.”

Rebecca 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?

IN THIS EPISODE
• Why motivation matters even more than productivity
• Escaping busywork by building better tools
• From R to Python to dashboards: Learning fast as a survival skill
• Reality check on AI in the IDE
• Why exploratory analysis still needs human intuition
• The 80/20 of coding: Automate the boring, protect the judgment
• Teaching beginners and earning trust
• The uncertain future of code

Rebecca Barter: Persistent learning, tool building, and ‘Will code even exist?’

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$Building Reproducible Pipelines in R | Will Landau | Data Science Hangout ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! Wed love to see you! This weeks guest was Will Landau, Senior Advisor of Innovative Statistics at Eli Lilly! Some topics covered in this weeks Hangout were building reproducible pipelines with the {targets} package, distributed and parallel computing with {crew} and {mirai}, designing trustworthy AI agents, choosing between Bayesian and frequentist approaches in clinical trials, and maintaining open source software in the age of AI. One community member asked: When do you reach for a Bayesian approach over a frequentist one in a clinical trial, and how do you explain that choice to stakeholders? Wills paraphrased answer: The familiar reason is prior information, whether you want to be opinionated about incorporating it through a rigorous prior or techniques like historical borrowing. But hes implemented plenty of Bayesian models on diffuse priors too, because they make early-phase decisions much easier to quantify. Rather than only asking whether a result is statistically significant, Bayesian models let you more conveniently answer the practical question: for example, the posterior probability of observing a large enough effect size, which is incredibly useful when deciding whether to move a therapy to a large Phase 3 registration study. You also dont need frequentist asymptotic approximations like the delta method, you can work directly with MCMC (markov chain monte carlo) and transformations on posterior samples. Resources mentioned in the video and chat: 🔗 targets Package book→ https://books.ropensci.org/targets/ 🔗 targets Package Documentation → https://docs.ropensci.org/targets/ 🔗 targets on GitHub → https://github.com/ropensci/targets 🔗 crew Package (distributed computing) → https://github.com/wlandau/crew 🔗 mirai Package (async, parallel, and distributed computing) → https://mirai.r-lib.org/ 🔗 Trusted Mini-Agents Guide (Will Landau) → https://wlandau.github.io/trusted-mini-agents/ 🔗 RTMB Package → https://cran.r-project.org/web/packages/RTMB/index.html 🔗 Will Landaus targets Workshop at R/Pharma 2023 → https://www.youtube.com/watch?v=cyF2dzloVLo&list=PLMtxz1fUYA5C0csy8-wTfFmRYQEB30vr3&index=12 🔗 Will Landau & Charlie Gao joint talk at posit::conf(2025) → https://www.youtube.com/watch?v=s_cBi155ZIQ 🔗 Data Science Lab episode with Charlie Gao on mirai → https://www.youtube.com/watch?v=Vf_jmY1tMWo&list=PL9HYL-VRX0oSeWeMEGQt0id7adYQXebhT&index=1 🔗 AI and the Invisible Newcomer in Open Source by Mara Averick → https://blog.stdlib.io/ai-and-the-invisible-newcomer-in-open-source/ 🔗 Kahns Algorithm for Topological Sorting → https://medium.com/@robhernandez5/kahns-algorithm-for-topological-sorting-26f29f2eaf48 🔗 XKCD: Dependency comic → https://xkcd.com/2347/ 🔗 MLU-Explain (visual machine learning explanations) → https://mlu-explain.github.io/ 🔗 Posit Academy → https://academy.posit.co/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! 💛 Timestamps: 00:00 Introduction 04:58 Can you talk about the targets package? 13:21 Youre doing great work to try to separate out that development support stuff from your day-to-day job and personal time and life. How the heck do you do it? 16:57 As someone whos primarily worked with tools like SnakeMake and Nextflow, why should I use targets, or what are some benefits of using targets over something like Nextflow? 20:37 How has Gen AI affected you as a package author and maintainer, mostly from other people submitting issues or PRs, and less about how you use it personally in your own work? 25:06 Do you want to talk a little bit about integrating Mirai to do things even faster? 29:10 How do you communicate uncertainty to people who want a single number? 31:24 What does an average day or week look like for you? How is your time divided between development, consulting, etcetera? 34:00 Have you had feedback from people using targets for agentic LLM workflows, for testing parameterization changes of RAG ingestion and associated changes to how the LLM instructions are dealing with that, or just anything in that creating and testing agentic workflows space? 37:15 When do you reach for a Bayesian approach over a frequentist one in a clinical trial, and how do you explain that choice to stakeholders? 41:32 Which Bayesian MCMC tool do you use? Do you use Stan, or the macros to get to Stan? 45:56 Is there a piece of career advice that you wish you knew before you started in data science, or anything that you think would be helpful for everybody else here?$