Uber Interview Questions
Practice 303 real Uber interview questions for 2026. Covers top categories — Coding & Algorithms, Analytics & Experimentation, Machine Learning, Behavioral & Leadership, Statistics & Math — across Software Engineer, Data Scientist, Machine Learning Engineer, and Technical Program Manager roles. Real questions from actual interviews with detailed solutions, focused guidance, and concrete interview preparation so you can practice the exact problem types Uber asks. Expect a coding-heavy loop for software engineering candidates: timed algorithm problems, online-assessment style OAs, and system-design tasks. For Software Engineer roles the recurring technical themes here are algorithm puzzles (kth-smallest-in-BST, knight/grid and reversal problems, prime-ending path counts), OA-style coding questions, and product-oriented design prompts such as a pickup-area driver queue and global nearby-restaurant search. Data Scientist questions center on membership/discount experiments, cold-start restaurant ratings and their launch evaluation, driver-acceptance modeling, and marketplace-impact analyses. Machine Learning Engineer prompts focus on completion-rate gaps, implementing attention and regression models, feed-ranking and restaurant-recommendation design, and pickup-location optimization. TPM items emphasize delivery-address fixes, competitive product comparisons, and leadership stories. Prepare by timing practice coding, rehearsing marketplace case studies, building short model write-ups, and polishing STAR examples for behavioral rounds.
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Design an Uber feature and analyze safety
You are interviewing for a Data Scientist summer internship at a ride-sharing marketplace. Part A: Product case Uber wants ideas for a new rider-facin...
Evaluate ETA Impact on Conversion
You are a Senior Data Scientist at a ride-hailing company such as Uber. ETA refers to the estimated pickup time shown to a rider before they decide wh...
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...
Model waiting-time abandonment via survival
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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...
Demonstrate business impact from a project
Behavioral Deep-Dive: Business Impact of a Prior Project (Technical Screen) Context: In a Data Scientist technical screen for a large two‑sided market...
Design an Uber A/B experiment end-to-end
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Develop test plan and TDD for word search
Design a Comprehensive Test Plan and TDD Workflow for Word-Search Functions Context and Assumptions We are testing two grid-based word search function...
Describe past impact and conflict handling
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Answer Common Behavioral Questions
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Count Islands After Land Additions
This question evaluates understanding of dynamic connectivity, incremental graph updates, and algorithmic efficiency when maintaining connected compon...
Formulate OR model to reduce driver backtracking
Define and reduce driver ‘backtracking’ in a marketplace. First, define a quantitative backtracking metric B per driver-hour from GPS and assignment l...
Diagnose location-sorted recommender causing revenue drop
This question evaluates skills in diagnosing production recommender systems, causal inference and experimentation, multi-objective ranking and safe ex...
Compute A/B sample size under clustering
A/B Test Sample Size With Unequal Allocation, Clustering, and Attrition Context You are planning a two-arm signup A/B test (binary outcome: convert vs...
Build and deploy an uplift targeting model
This question evaluates a candidate's ability to design and deploy uplift/causal targeting models, covering causal inference, uplift estimation, pre-t...
Estimate price–ETA trade-offs causally
Causal Effect Between Price and Expected Arrival Time (ETA) in a Real-Time Ride-Hailing Marketplace Objective Estimate the causal relationship between...
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...
Analyze results and large p-values correctly
Experiment Analysis Plan: User-Level ITT with Robust Inference, Variance Reduction, Ratios, Skew, Non-Compliance, and Decision Framework Context You r...
Navigate urgency, priorities, and conflict
Behavioral & Leadership: Ambiguity, Dependencies, and Execution Under Pressure You will describe one real project where you faced high ambiguity and c...