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Investigate Causes of Cold Food Deliveries and Solutions

DoorDashData Scientist · Technical Screen
MediumData ScientistTechnical ScreenAnalytics & Experimentation

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This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Investigate Causes of Cold Food Deliveries and Solutions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Scenario

Customers complain that delivered food often arrives cold. As the data scientist for the delivery quality (Dasher) team, you must diagnose the problem and design a solution.

Question

How would you investigate the root causes of cold food deliveries? Which metrics would you track, and what data would you pull? Design an experiment to test a mitigation (e.g., insulated bags, optimized routing). Detail hypothesis, treatment, control, unit of randomization, success metrics, and runtime calculation.

Hints

Frame with funnel analysis (prep, pickup, travel time), define quantitative temperature proxy, consider staged A/B test across zones, monitor delivery time, reorder-rate, and complaint-rate.