The closed-book coding round is dying, and that is a good thing. Data science interviews at Meta, Shopify, Adobe, and Google are moving toward AI-assisted pair programming, where candidates use tools in real time to solve problems rather than recite syntax from memory. This shift reframes the interview from a memory test to a working session, and it aligns with how data science actually happens now. If you are preparing for these rounds, the takeaway is direct: your value is no longer in recalling a function signature, but in structuring a problem, interpreting output, and steering an AI collaborator toward a sound answer.
This change mirrors a broader theme we have been tracking in our Your weekly guide to entering data science with confidence: the field rewards adaptability over rote knowledge. The old interview format assumed that what you could type from memory reflected your true skill. In practice, that rewarded cramming and penalized experienced practitioners who rely on documentation just like everyone else. The new format, as demonstrated in the un-gated video and Colab notebook shared by a candidate who went through these rounds in Spring 2026, treats the interview like a mini case study. You are not asked to produce code in a vacuum; you are asked to solve a business problem with the same tools you would use on the job. That is a more honest signal of competence.
There is a practical consequence for candidates: you must practice with AI at your side, not against it. The challenge is no longer "write a model from scratch" but "use this assistant to build a model that answers a specific question, and explain why your choices make sense." That requires a different kind of preparation. You need to be fluent in prompting, quick to spot when the AI produces a flawed approach, and confident enough to override it. The exploration of DuckDB's direct access to Hugging Face datasets is a useful example of the mindset shift: instead of memorizing data-loading commands, you learn what tools exist and how to call them efficiently. The same logic applies in an interview. Knowing that a library can handle a task is more important than knowing its every parameter.
The open question is how far this trend goes. If AI handles implementation, what does the interviewer evaluate? The answer appears to be judgment: how you frame the problem, what assumptions you state, how you validate results, and how you communicate trade-offs. That is a harder skill to fake, and it is the one worth building. For anyone preparing now, the specific detail to watch is how companies calibrate the difficulty of these sessions. If they keep raising the complexity of the business context, the interview becomes less about coding and more about consulting. That is a future worth preparing for, and it is one where your ability to think clearly matters more than your ability to type fast.