Chewy Data Scientist Interview Guide

Chewy Data Scientist interviews reported by candidates combine project discussion, business reasoning, and technical depth rather than relying on a single uniform coding format. One candidate described a recruiter screen and general screen before a final loop; another described two phone screens followed by an onsite. The reported total varies by candidate, so use the stages as a preparation framework rather than a fixed itinerary.

Be ready to explain your own work in detail. A hiring-manager screen was described as behavioral and high-level technical conversation, with repeated follow-up questions on past projects. Practice explaining the decision you made, the data you used, the alternatives you considered, and how you evaluated results. Behavioral discussion may also cover conflict and teamwork.

Technical preparation should connect modeling to a business decision. One candidate worked through a marketing-campaign problem: selecting among campaigns, choosing data and models, and measuring revenue success as new constraints were introduced. Another reported open-ended business questions alongside Pandas coding, machine learning, time series, and operations research. Structure responses from objective and data through method and success metric, then adapt when assumptions change.

The coding format is not fully consistent across reports. One candidate reported no live coding and use of a shared notepad where applicable, while another reported Pandas coding. Practice communicating code or pseudocode clearly while narrating your reasoning, and pair that with concise explanations of ML and forecasting choices.