Uber product sense interview questions

Sample questions with short answers

Tap a question to reveal the short answer and key points.

1. 1How would you increase driver retention in a city with high churn?MediumUber-specificShow short answer

   Short answer

   Segment drivers by tenure and earnings; churn usually concentrates in the first weeks. Identify pain points — earnings unpredictability, waiting time, app friction — and prioritise one, e.g. guaranteed-earnings onboarding. Metrics: 30-day active rate and weekly hours; guardrails: cost per retained driver and rider wait time.

   Interviewers listen for

   • segment by tenure and earnings
   • target a specific pain point
   • retention metric with cost guardrail
2. 2Should Uber launch a subscription that waives surge pricing?HardUber-specificShow short answer

   Short answer

   Frame who benefits (frequent commuters) and the risk (adverse selection, lost surge revenue, driver supply at peak). Propose a capped version — a monthly fee for surge discounts up to a limit — and an experiment measuring rides per subscriber, margin per ride and driver supply. State the decision criteria before launch.

   Interviewers listen for

   • target segment and adverse selection risk
   • capped design to protect margin
   • experiment with pre-set decision criteria
3. 3How would you improve a product you use every day?MediumAsked everywhereShow short answer

   Short answer

   Pick a product, state its goal and the user segment you will focus on, then list their pain points and prioritise one by reach and impact. Propose two or three solutions, pick one, and define a success metric plus a guardrail metric. Call out the trade-offs and how you would test it. The structure matters more than the specific idea.

   Interviewers listen for

   • define goal and user segment
   • prioritise pain points by impact
   • success metric and guardrail metric
4. 4A key metric dropped 15% week over week. How do you investigate?MediumAsked everywhereShow short answer

   Short answer

   First check data integrity — tracking changes, logging bugs, timezone or attribution shifts. Then segment: platform, geography, new vs returning users, release version. Correlate with internal events (launches, experiments, pricing) and external ones (holidays, competitors, outages). Form a hypothesis, validate it, and only then propose a fix.

   Interviewers listen for

   • rule out data or tracking issues first
   • segment the drop
   • correlate with internal and external events
5. 5How do you decide what to build next when engineering capacity is limited?HardAsked everywhereShow short answer

   Short answer

   Anchor on the product goal for the quarter, then score candidates on user value, business impact, confidence and effort (RICE or a simpler impact/effort grid). Protect a slice of capacity for tech debt and reliability. Explain the trade-offs to stakeholders with the data behind the ranking, and revisit when new evidence arrives.

   Interviewers listen for

   • tie to the quarter's goal
   • score by impact, confidence and effort
   • reserve capacity for debt and reliability