Design metrics and an experiment for Eats donations

Q: Design metrics and an experiment for Eats donations

This question evaluates analytics and experimentation skills—specifically metric definition (numerators/denominators and funnel placement), hypothesis formulation, randomized experiment design, and identification of confounders—in the Analytics & Experimentation domain at a mid-to-senior data scientist abstraction level.

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

Uber Eats is considering a new feature: when a user places an order, they can optionally add a donation to the merchant (or a merchant-selected cause) during checkout.

You are the DS owning the evaluation.

Propose primary success metrics and guardrail metrics . Be explicit about definitions (numerators/denominators) and where in the funnel they are measured.
State 2–4 plausible hypotheses (including at least one potential negative effect).
Design an experiment to evaluate the feature (randomization unit, experiment population, variants, duration/power considerations).
List major confounders/biases or marketplace effects that could mislead conclusions and how you would address them.

Design metrics and an experiment for Eats donations

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