Analytics & Experimentation Interview Questions

Showing 511–540 of 560 results

• TikTokHard·Data Scientist
  Measure Billboard Campaign Effectiveness and Engagement Quantification

  Evaluates offline marketing measurement for a billboard campaign, including how to define attributable engagement, estimate exposed population, and forecast incremental traffic, sign-ups, retention, revenue, and ROI. Strong answers use geo-lift, matched markets, or synthetic controls rather than direct attribution alone.

  Analytics & ExperimentationOnsite
• TikTokHard·Data Scientist
  Prove conversion ads value via incrementality

  This question tests your ability to design a robust incrementality experiment to measure the causal impact of advertising, accounting for selection bias and interference.

  Analytics & ExperimentationTechnical ScreenCausal InferenceA/B TestingAds Measurement
• TikTokHard·Data Scientist
  Test 15s to 60s video length change

  This question evaluates a data scientist's competency in experimental design, causal inference, metric definition and instrumentation, power/MDE calculations, interference and contamination handling on multi‑sided platforms, and rollout planning.

  Analytics & ExperimentationOnsite
• TFTools For HumanityEasy·Data Scientist
  Detect and evaluate "stolen" posts

  This question evaluates data-science competencies in instrumentation and feature design, content-similarity detection and labeling, causal inference for experiments, measurement of harms, and model evaluation, and belongs to the Analytics & Experimentation domain.

  Analytics & ExperimentationTechnical Screen
• TubiHard·Data Scientist
  Determine A/B test sample size drivers

  This question evaluates statistical power and sample size determination for A/B testing, covering hypothesis testing for proportions, derivation using normal approximations, the impact of MDE, α and power choices, allocation ratios, cluster design (ICC and design effect), group‑sequential monitoring, and adjustment for overdispersed count outcomes.

  Analytics & ExperimentationTechnical Screen
• TubiHard·Data Scientist
  Handle multiple experiment metrics and communication

  This question assesses the ability to design a multi-metric experiment, manage the false positive rate under multiple testing, and communicate trade-off decisions to non-technical stakeholders.

  Analytics & ExperimentationTechnical ScreenA/B TestingStatistical SignificanceMultiple Comparisons
• TuroHard·Data Scientist
  Identify Booking Drivers

  This question evaluates skills in product analytics, causal inference, feature importance modeling, metric definition, and experimental validation within a marketplace context.

  Analytics & ExperimentationOnsite
• UberMedium·Software Engineer
  Define and integrate room ranking factors

  Define and integrate room ranking factors evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• UberMedium·Data Scientist
  Design Pricing Model Experiment

  This question evaluates experimental design and causal inference competencies for marketplace pricing, covering metric selection, guardrail definition, interference and switchback designs, and sample-size and treatment-window tradeoffs.

  Analytics & ExperimentationTechnical Screen
• UberMedium·Data Scientist
  Design Rideshare Marketplace Causal Analyses

  This question evaluates a data scientist's skills in experimental design and causal inference, marketplace metrics and diagnostics, fairness and safety impact assessment, incentive and policy evaluation, and ETA modeling and forecast quality measurement.

  Analytics & ExperimentationOnsite
• UberHard·Data Scientist
  Design an ETA experiment under interference

  This question evaluates a data scientist's competency in experimental design and causal inference for two-sided marketplaces, focusing on interference, partial compliance, metric definition, randomization strategy, instrumentation, estimation, and power calculations within the Analytics & Experimentation domain.

  Analytics & ExperimentationTechnical Screen
• UberHard·Data Scientist
  Design an experiment with marketplace network effects

  This question evaluates a candidate's competency in causal experiment design for networked two‑sided marketplaces, focusing on interference and spillover management, cluster randomization choices, metric definition, power/sample‑size calculations, bias‑reduction approaches, and diagnostic checks.

  Analytics & ExperimentationTechnical Screen
• UberEasy·Data Scientist
  Design 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
• UberHard·Data Scientist
  Design 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
• UberHard·Data Scientist
  Design promo experiment and explain correlation

  This problem tests your ability to distinguish correlation from causation in observational data and to design a robust A/B test to measure the incremental impact of a new rider promotion.

  Analytics & ExperimentationTechnical ScreenCausal InferenceA/B TestingExperimentation
• UberMedium·Data Scientist
  Determine Sample Size for Promotion Campaign A/B Test

  This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Determine Sample Size for Promotion Campaign A/B Test states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• UberMedium·Data Scientist
  Evaluate ETA Impact on Conversion

  This question evaluates a data scientist's skills in causal inference, experimental design, and marketplace analytics, including understanding of confounding, selection bias, interference, and the distinction between correlation and causation.

  Analytics & ExperimentationTechnical Screen
• UberMedium·Data Scientist
  Evaluate 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
• UberMedium·Data Scientist
  Evaluate Rider-Incentive Program Impact with Key Metrics

  Uber data scientist analytics prompt on evaluating a rider-incentive program, covering experiment design, rider and driver metrics, matching quality, ROI, marketplace externalities, spillovers, and guardrails.

  Analytics & ExperimentationTechnical Screen
• UberMedium·Data Scientist
  Evaluate UberEATS priority delivery and membership

  This question tests the ability to design an experiment for a marketplace feature and evaluate its impact on key business metrics like conversion, frequency, and total contribution margin.

  Analytics & ExperimentationTechnical ScreenExperimentationMarketplaceCausal Inference
• UberMedium·Data Scientist
  Evaluate a cold-start rating launch

  This question tests the ability to design an evaluation framework for a new feature in a two-sided marketplace while accounting for spillover effects and metrics that represent different stakeholder interests.

  Analytics & ExperimentationTechnical ScreenA/B TestingMarketplace AnalyticsCausal Inference
• UberMedium·Data Scientist
  Evaluate business value of lower ETA

  This question tests the ability to design an experiment that isolates the causal impact of reduced ETA on user retention and to translate these metrics into business value.

  Analytics & ExperimentationTechnical ScreenExperimentationCausal InferenceMarketplace Analytics
• UberHard·Data Scientist
  Evaluate impact without randomized experiments

  This question evaluates a data scientist's competence in causal inference and identification strategies for estimating promotion effects from observational longitudinal data, emphasizing challenges such as staggered rollout, interference (spillovers), seasonality, and uncertainty quantification.

  Analytics & ExperimentationTechnical Screen
• UberMedium·Data Scientist
  Evaluate 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
• UberHard·Data Scientist
  Explain and validate A/B test assumptions

  This question evaluates proficiency in experimental design, causal inference, and statistical diagnostics for A/B testing, covering competencies such as randomization integrity, SUTVA/interference, noncompliance, time-varying effects, metric missingness, sequential testing, and heterogeneous treatment effects.

  Analytics & ExperimentationTechnical Screen
• UberEasy·Data Scientist
  How to experiment on ETA reduction

  This problem tests the ability to design an A/B test for product improvements and address potential confounding factors like selection bias and spillover effects.

  Analytics & ExperimentationTechnical ScreenA/B TestingCausal InferenceMarketplace Dynamics
• UberHard·Data Scientist
  Improve Estimated Time of Arrival for Uber Riders

  This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Improve Estimated Time of Arrival for Uber Riders states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• UberHard·Data Scientist
  Investigate 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
• UberEasy·Data Scientist
  Measure feature impact with switchback, PSM, and CACE

  This question evaluates proficiency in causal inference and experimental design for product metrics, specifically testing knowledge of switchback experiments, time-series adjustments (seasonality and autocorrelation), propensity score methods for observational comparisons, and complier-average causal effect (LATE/CACE) estimation; it belongs to the Analytics & Experimentation and Data Science domain. It is commonly asked because it probes handling of real-world complications—time and city heterogeneity, non-compliance, and derived-metric inference—and tests both conceptual understanding of causal assumptions and practical application of statistical adjustment and inference.

  Analytics & ExperimentationTechnical Screen
• UpstartEasy·Data Scientist
  Estimate impact without experiments and pick variant

  This question evaluates causal inference and experimental-analysis competencies in Analytics & Experimentation and Data Science, covering observational estimands, causal identification assumptions and biases, uncertainty quantification for A/B/C tests, multiple-comparisons reasoning, and post-launch forecasting and monitoring.

  Analytics & ExperimentationTechnical Screen