Chewy Data Scientist Interview Guide

We've seen Chewy lean much more heavily on applied machine learning judgment than on algorithmic trickery. The strongest signal from candidate experiences is that interviewers want to hear how you frame a business problem, choose the right data, and connect model output to revenue or campaign performance. One candidate described a marketing-revenue case where the conversation kept expanding as new details were introduced, which suggests the team is evaluating whether you can reason through ambiguity in a way that stays grounded in the business.

A recurring theme is that Chewy seems to care about whether you can make practical tradeoffs, not just name the fanciest model. Candidates reported questions around marketing efficiency, email and banner ad strategy, and even explaining random forests in plain language. That mix tells us they are listening for clear model selection logic and a credible measurement plan more than for textbook definitions. If your answer jumps straight to tooling without explaining why a campaign should be compared, how success should be measured, or what data would actually move the decision, you will likely feel the room go flat.

We also notice a consistent emphasis on collaboration and communication. Multiple candidates mentioned behavioral conversations about conflict, teamwork, and culture fit, alongside a remote format where the interviewer would actively feed more information into the case. That pattern matters: Chewy appears to value people who can think out loud, adapt as the problem changes, and stay structured without becoming rigid. The candidates who do best here usually sound like someone the business could hand a messy growth question to and trust the answer would be both thoughtful and usable.