Design and power an incentive experiment
Company: Uber
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: hard
Interview Round: Onsite
Offer timing and efficacy of benefits: Design an experiment to test whether a bonus (e.g., cash or queue priority) increases the share of paperwork-complete candidates who complete their first action within 14 days. Specify eligibility/exclusions; unit of randomization; treatment arms (control, bonus-now, bonus-after-first-action, staged bonus); stratification to separate bonus-dependent vs. intrinsically motivated users; primary/secondary metrics and guardrails (quality, fraud, downstream retention); handling noncompliance, spillovers, and interference; stopping rules and decision criteria. Compute the minimum per-arm sample size to detect a +3 percentage-point lift from a 20% baseline at 90% power, α=0.05 using a two-proportion z-test—show formula and a numeric result. Explain when to roll out benefits broadly vs. target narrowly based on heterogeneous treatment effects.
Quick Answer: This question evaluates experimental design, causal inference, A/B testing and statistical power calculation skills, including defining eligibility, unit of randomization, treatment arms, stratification, metric selection with guardrails, handling noncompliance and interference, and interpreting heterogeneous treatment effects.