Doordash Data Scientist Interview Guide

DoorDash Data Scientist interviews reported by candidates center on two connected skills: practical SQL reasoning and structured product analytics. Initial screens pair SQL with a product, diagnostic, or experimentation case, although the order and format vary. Reported SQL topics include window functions, date calculations, and interpreting a pre-written query; one newer account characterized the SQL logic as basic. Expect to explain what a query does rather than treating the exercise as coding alone.

The case material is notably marketplace-specific. Candidates describe prompts about declining successful deliveries and evaluating a new feature. A clear diagnosis before a proposed test or solution is the recurring pattern: define the metric, break the problem into relevant marketplace factors, prioritize hypotheses, and then discuss how an experiment or operational response could evaluate a change. Experimentation discussions may also cover setup, guardrails, and undesirable outcomes.

Candidates who progressed further describe multiple product-case conversations, sometimes alongside a behavioral interview or a conversation with a product manager. Prepare concise examples of cross-functional work and impact, but keep the emphasis on communicating analytical judgment. Reports suggest that apparently simple cases can still produce detailed follow-up questions, so practice giving a crisp structure before adding detail. The exact sequence varies by candidate and progression stage.