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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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 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 an Uber A/B experiment end-to-end
Experiment Design: Pickup ETA Card Redesign Context: After a rider requests a trip, the app shows a pickup ETA card. The hypothesis is that clearer ET...
Design a switchback and choose block length
Switchback Experiment Design: Airport Pickup Pricing with Spillovers Context You are designing a switchback (time-based A/B) experiment for airport pi...
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...
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...
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...
Design an ETA experiment under interference
Experiment Design: Estimating Causal Impact of a New Rider ETA Model in a Two-Sided Marketplace Context You are testing a new rider ETA model that cha...
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...
Choose between A/B and switchback for spillovers
Airport Driver-Queue Algorithm: Experiment Design and Causal Reasoning Background A new driver-queue algorithm is being tested at a single airport wit...
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...
Define ride success metric for Uber
Define a single primary metric for "Uber ride success" Design one primary, comparable metric for ride success across markets and cohorts. Provide: 1) ...
Measure Impact of Updated Rider ETA Algorithm
Scenario A ride-hailing company has updated its rider ETA-prediction algorithm (the ETA shown to riders before they request a trip) and wants to quant...
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....
Measure driver experience quantitatively
Driver Experience Index (DEI) — Design, Validation, Debiasing, and Experimentation Context: You are a Data Scientist at a ride-hailing company. Define...
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 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 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...
Evaluate Push Notification Impact on Rideshare Supply Shortages
Experiment Design: Push Notifications for Airport Surge Shortage Resolution Context When rider demand at the airport exceeds available driver supply, ...