DoorDash·Data Scientist·Technical Phone Screen
Jun 2026
DoorDash data science case interview focused entirely on diagnosing a drop in successful orders for a food delivery marketplace. Heavy emphasis on structured thinking across demand, supply, and operational dimensions, plus experiment design. No behavioral questions from what I remember.
- Successful orders in one geographic market have been declining for four weeks. What could be causing this?
- Which metrics and data slices would you look at first when investigating this kind of drop?
- How would you tell apart a demand-side problem, a supply-side problem, an operational issue, and a data instrumentation bug?
- Walk me through one or two experiments you would run to validate a root cause and try to improve the metric. Include your primary metric, guardrails, and how you'd handle ambiguous results.
“This is where I spent way too long listing possibilities without organizing them.” The rest of the author's notes on Data Scientist interview at DoorDash, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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