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Improve Estimated Time of Arrival for Uber Riders

UberData Scientist · Technical Screen
HardData 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 Improve Estimated Time of Arrival for Uber Riders states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Scenario

Uber ride-hailing: understanding and improving the Estimated Time of Arrival (ETA) shown to riders.

Question

List driver-, rider-, and platform-level factors that can affect displayed ETA. From a product-management perspective, why is ETA an important metric? You observe a positive correlation between ETA length and ride conversion rate; provide possible internal and external explanations. Design an experiment to measure the causal impact of ETA on rider conversion. How would you choose the switchback window length in that experiment and what are the trade-offs of shorter versus longer windows?

Hints

Think causal vs. correlational, switchback vs. A/B, geographic or temporal randomization, metric sensitivity, supply–demand dynamics.