Uber Analytics & Experimentation Interview Questions
Uber Analytics & Experimentation interview questions focus on experimentation at scale inside a two‑sided marketplace where small measurement mistakes can have big business consequences. Interviewers typically evaluate your ability to design rigorous A/B tests and causal analyses (unit of randomization, sample size, guardrail metrics, and variance‑reduction), your statistical intuition for significance and power, and your product and operational judgment about interference, ramping and rollback. Expect a mix of case-style experiment design prompts, metric-definition and root‑cause scenarios, and hands‑on questions that probe your SQL/stats fluency and ability to interpret noisy results. For interview preparation, emphasize experiment design fundamentals, common pitfalls (SRM, interference, peeking, non‑normal metrics), and clear communication of assumptions and tradeoffs. Practice framing goals, choosing primary and guardrail metrics, sketching sample‑size calculations, and describing rollout plans and safety checks. Walk through a few real or mock investigations end‑to‑end—hypothesis to analysis to recommendation—so you can explain choices concisely to product and engineering partners under time pressure.

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Define market-only rider experience metrics
Market-only Rider Experience Metrics and Market Balance Index (MBI) You are designing a metric suite for a rides marketplace where "rider experience" ...
Design an RCT for app-open discount
Design an RCT for an "X dollars off on app open" promotion in a two‑sided marketplace Context You operate a two‑sided marketplace mobile app (e.g., ri...
Explain and validate A/B test assumptions
A/B Test Validity: Core Assumptions, Violations, Diagnostics, and Mitigations You are designing and evaluating an online A/B test for a large, multi-s...
Investigate ride declines and test free trials
LA Shared Rides Down 10% MoM — Diagnostic And Action Plan Context: The Los Angeles market is seeing a 10% month-over-month decline in completed rides ...
Design an experiment with marketplace network effects
Causal Experiment Design for a Two‑Sided Marketplace with Interference You are designing a causal experiment for a new networked product in a two‑side...
Design and Evaluate an Experiment on Surge
Experiment Design: Surge-Cap Algorithm for NYC (20:00–22:00) Context A new surge-cap algorithm is proposed to improve rider trip completion in NYC dur...
Evaluate Rider-Incentive Program Impact with Key Metrics
Scenario You are designing an evaluation for a new rider-incentive program in a two‑sided ride‑hailing marketplace (riders request trips; drivers supp...
Define and integrate room ranking factors
Design a Room-Ranking System for Meeting Requests Context You are building a service that assigns conference rooms to meeting requests across multiple...
Estimate causal effect with interference
A/B Test With Noncompliance and Interference: Causal Effect of Surge Recommendations on Completed Trips Context You ran an A/B test that assigned some...
Design and power an incentive experiment
Experiment: Timing and Efficacy of Onboarding Benefits Context You operate a two-sided marketplace with supply-side candidates who often complete requ...
Design an A/B test for promo-targeting models
Experiment Design: Compare Two Ranking Models (M1 vs M0) for $5 Promotions Context You have two models, M0 (current) and M1 (new), that rank users for...
Design a robust email A/B test
A/B Test Design: New Email Subject Line for Weekly Campaign You manage a weekly email campaign to 10 million users. Baseline unique click-through rate...
Design metrics and A/B test for maps and ETA
Context You work on Uber’s driver app. Drivers can navigate using either Google Maps or Uber Maps. Separately, Uber shows riders an estimated time of ...
Design station experiment with interference and rush-hour spillovers
Experiment Design Under Interference for an In‑Station Ordering Pilot Context (Completed) You are evaluating two competing in‑station ordering feature...
Analyze T2 Results and Recommend Launch Strategy
A/B Test Interpretation, Launch Decision, Segmentation, and Multi-Experiment Error Control Context You ran two A/B tests on an e-commerce platform: - ...
Measure feature impact with switchback, PSM, and CACE
You work at a ridesharing company and want to measure the impact of a new membership feature on rides-per-user (RPU). Part A — Switchback experimentat...
Design promo experiment and explain correlation
You work on a ride-hailing marketplace (drivers + riders). Answer the following analytics and experimentation questions. 1) Interpret a surprising cor...
Improve Estimated Time of Arrival for Uber Riders
Scenario Ride-hailing platform: understanding and improving the Estimated Time of Arrival (ETA) shown to riders. Question 1) List driver-, rider-, and...
Evaluate impact without randomized experiments
Estimating a Promotion's Causal Effect Without an Experiment Context You need to estimate the causal impact of a marketing promotion on engagement (e....
Evaluate Promotion Campaign Effectiveness with A/B Testing
A/B Testing a San Francisco–Only January Promotion Background A consumer marketplace app plans to run a January 2024 promotion limited to San Francisc...