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Analytics & Experimentation Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 211–218 of 218 results
- UberEasyData ScientistDesign and Test a New Feature
This question evaluates a candidate's ability to design a new product feature for a ride-hailing platform and to translate product goals into measurable outcomes, testing competencies in product sense, metric definition, experiment and causal-inference design, segmentation, and two-sided marketplace trade-off analysis.
Analytics & ExperimentationOnsite - UberHardData ScientistDesign and power an incentive experiment
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.
Analytics & ExperimentationOnsite - UberMediumData ScientistEvaluate Marketplace Changes
This problem tests your ability to design an A/B test for a two-sided marketplace, focusing on network effects, metric selection, and identifying potential interference between treatment and control groups.
Analytics & ExperimentationOnsiteExperimentationA/B TestingMarketplace - UberMediumData ScientistEvaluate marketplace interventions
This question tests the ability to design an experimentation framework for a two-sided marketplace, specifically addressing interference between supply and demand and selecting appropriate success metrics.
Analytics & ExperimentationOnsiteExperimentationMarketplaceCausal Inference - UberHardData ScientistInvestigate ride declines and test free trials
This question evaluates proficiency in product analytics, cohort and funnel analysis, retention and supply diagnostics, pricing and subscription economics, and experimentation design.
Analytics & ExperimentationOnsite - WaymoMediumData ScientistConservative Wait Estimates Cut Conversion and Pickup Time: Did Efficiency Improve?
A ride service showed riders conservative pickup-time estimates; afterwards conversion fell 5% and actual time to pickup fell 25%. Decide whether fleet efficiency really improved and list alternative explanations, testing selection effects and metric design.
Analytics & ExperimentationOnsite - WaymoEasyData ScientistHow validate a driving simulation is realistic?
This question tests the ability to design an evaluation framework to compare simulated vehicle behavior against real-world sensor and trajectory logs to ensure high-fidelity simulation.
Analytics & ExperimentationOnsiteSimulationValidationStatistics - WaymoMediumData ScientistWelch's t-Test p-Value for a Routing Experiment on Time to Pickup, and Its Validity
Filter rides to one city and month, join users to their experiment variants, and compute a Welch's t-test p-value on time to pickup in Python. Then judge whether the routing test supports a conclusion and design a better experiment for a shared fleet.
Analytics & ExperimentationOnsite
Uber
Waymo