Improve Estimated Time of Arrival for Uber Riders

Q: Improve Estimated Time of Arrival for Uber Riders

This question evaluates a data scientist's proficiency in causal inference, experimentation design, product metrics analysis, and operational factors affecting estimated time of arrival within the Analytics & Experimentation domain.

Q: How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

Question

Scenario

Ride-hailing platform: 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. Explain why a switchback design is appropriate, how you would randomize, and what metrics to track.
How would you choose the switchback window length, 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.

Improve Estimated Time of Arrival for Uber Riders

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Improve Estimated Time of Arrival for Uber Riders

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