Analytics & Experimentation interview questions

Showing 511–540 of 560 results

• Measure Billboard Campaign Effectiveness and Engagement QuantificationHard

  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.

  TikTokData ScientistAnalytics & ExperimentationOnsite
• Prove conversion ads value via incrementalityHard

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

  TikTokData ScientistAnalytics & ExperimentationTechnical Screen
• Test 15s to 60s video length changeHard

  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.

  TikTokData ScientistAnalytics & ExperimentationOnsite
• Detect and evaluate "stolen" postsEasy

  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.

  Tools For HumanityData ScientistAnalytics & ExperimentationTechnical Screen
• Determine A/B test sample size driversHard

  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.

  TubiData ScientistAnalytics & ExperimentationTechnical Screen
• Handle multiple experiment metrics and communicationHard

  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.

  TubiData ScientistAnalytics & ExperimentationTechnical Screen
• Identify Booking DriversHard

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

  TuroData ScientistAnalytics & ExperimentationOnsite
• Define and integrate room ranking factorsMedium

  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.

  UberSoftware EngineerAnalytics & ExperimentationOnsite
• Design Pricing Model ExperimentMedium

  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.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• Design Rideshare Marketplace Causal AnalysesMedium

  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.

  UberData ScientistAnalytics & ExperimentationOnsite
• Design an ETA experiment under interferenceHard

  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.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• Design an experiment with marketplace network effectsHard

  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.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• Design and Test a New FeatureEasy

  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.

  UberData ScientistAnalytics & ExperimentationOnsite
• Design and power an incentive experimentHard

  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.

  UberData ScientistAnalytics & ExperimentationOnsite
• Design promo experiment and explain correlationHard

  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.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• Determine Sample Size for Promotion Campaign A/B TestMedium

  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.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate ETA Impact on ConversionMedium

  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.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate Marketplace ChangesMedium

  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.

  UberData ScientistAnalytics & ExperimentationOnsite
• Evaluate Rider-Incentive Program Impact with Key MetricsMedium

  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.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate UberEATS priority delivery and membershipMedium

  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.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate a cold-start rating launchMedium

  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.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate business value of lower ETAMedium

  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.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate impact without randomized experimentsHard

  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.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate marketplace interventionsMedium

  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.

  UberData ScientistAnalytics & ExperimentationOnsite
• Explain and validate A/B test assumptionsHard

  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.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• How to experiment on ETA reductionEasy

  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.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• Improve Estimated Time of Arrival for Uber RidersHard

  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.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• Investigate ride declines and test free trialsHard

  This question evaluates proficiency in product analytics, cohort and funnel analysis, retention and supply diagnostics, pricing and subscription economics, and experimentation design.

  UberData ScientistAnalytics & ExperimentationOnsite
• Measure feature impact with switchback, PSM, and CACEEasy

  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.

  UberData ScientistAnalytics & ExperimentationTechnical Screen
• Estimate impact without experiments and pick variantEasy

  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.

  UpstartData ScientistAnalytics & ExperimentationTechnical Screen