Data Scientist Interview Questions

Showing 391–420 of 513 results

• PayPalHard·Data Scientist
  Design an A/B for ATO rule

  This question evaluates a data scientist's experiment-design and statistical-analysis competencies, including cluster-aware randomization, power/sample-size calculation, sequential monitoring, and translating detectable effects into net business impact for a real-time account-takeover prevention rule on a payment platform.

  Analytics & ExperimentationTechnical Screen
• PayPalMedium·Data Scientist
  Design and Analyze A/B Test for Cashback Program

  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 Design and Analyze A/B Test for Cashback Program states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• PayPalEasy·Data Scientist
  Design and evaluate a fraud detection strategy

  This problem tests your ability to design a holistic fraud detection strategy by balancing technical model metrics with business-critical operational trade-offs.

  Analytics & ExperimentationTechnical ScreenFraud DetectionClassificationMetrics
• PayPalEasy·Data Scientist
  Design metrics and experiment for donation feature

  A PayPal data science onsite case asking you to design metrics and a randomized experiment for an optional checkout donation feature on a food-ordering platform. Covers goal and hypothesis framing, a primary/diagnostic/guardrail metric hierarchy with precise definitions, user-level experiment design with power and variance-reduction, confounder/marketplace-bias handling, and a launch decision framework.

  Analytics & ExperimentationOnsite
• PayPalEasy·Data Scientist
  How to evaluate a new homepage feature

  A PayPal data science technical screen on designing an A/B test to evaluate a new homepage feature. It covers experiment design and randomization, primary/secondary/guardrail metric selection with tradeoffs, sample-size and power estimation, and pitfalls like novelty effects and interference. The solution emphasizes value-aligned metrics (payment completion over CTR) and fraud guardrails specific to a payments product.

  Analytics & ExperimentationTechnical Screen
• PayPalMedium·Data Scientist
  Master A/B Testing: Key Concepts and Methodologies Explained

  Evaluates practical A/B testing and causal inference fundamentals for online products. Strong answers cover p-values, errors, power, workflow, Simpson's paradox, metrics, and observational causal methods.

  Analytics & ExperimentationOnsite
• PayPalEasy·Data Scientist
  Reduce airport ride cancellations under causal constraints

  A PayPal Data Scientist onsite product-sense and causal-inference case: design a measurement framework, hypotheses, and experimentation strategy to reduce airport ride cancellations in a ride-hailing marketplace without harming marketplace health. It tests metric trees, interference-aware experiment design (switchback, partial-population, encouragement/IV), satisfaction proxies, confounder handling, and stakeholder communication.

  Analytics & ExperimentationOnsite
• PinterestHard·Data Scientist
  Analyze a geo rollout and interpret charts

  This question evaluates causal inference and product analytics competencies — specifically specification of causal estimands and difference-in-differences, segmented (interrupted) time-series regression, power and duration calculations, spillover and donor-pool diagnostics, and selection of guardrail metrics.

  Analytics & ExperimentationOnsite
• PinterestHard·Data Scientist
  Decide if ad load is optimized

  This question evaluates competency in analytics and experimentation, covering experiment design, metric and guardrail definition, causal inference, statistical testing, segmentation for heterogeneous effects, and balancing user experience versus monetization.

  Analytics & ExperimentationOnsite
• PinterestMedium·Data Scientist
  Design and Evaluate a Home Carousel

  This question evaluates a candidate's expertise in product analytics, experimentation design, metric definition, and instrumentation for measuring the impact of a new UI feature.

  Analytics & ExperimentationTechnical Screen
• PinterestHard·Data Scientist
  Design and Interpret a Video Pin Experiment

  This question tests the ability to design a balanced A/B experiment for a two-sided marketplace (creators and viewers) and synthesize conflicting metrics into a launch decision.

  Analytics & ExperimentationTechnical ScreenA/B TestingProduct MetricsGuardrails
• PinterestMedium·Data Scientist
  Design and assess a video-pin increase experiment

  A Pinterest Data Scientist technical-screen question on Analytics & Experimentation: design a rigorous A/B test for increasing video-pin share in the Home Feed — unit of randomization, primary metric and guardrails, power/MDE, sequential analysis, and a quasi-experiment fallback — then interpret two 14-day readouts and recommend ship, iterate, or stop. It rewards balancing engagement lifts (CTR) against guardrail regressions (retention, complaints, crash rate), multiple-testing robustness, and cross-unit (per-impression vs per-session) trade-offs.

  Analytics & ExperimentationTechnical Screen
• PinterestHard·Data Scientist
  Design metrics and experiment for Shopping launch

  This question evaluates a data scientist’s competency in experimental design and product analytics, including metric selection and precise definitions, guardrail and funnel diagnostics, spillover and novelty handling, power/MDE estimation, and multi-objective decision framing.

  Analytics & ExperimentationTechnical Screen
• PinterestHard·Data Scientist
  Diagnose CTR drop after recommendation launch

  This question evaluates a data scientist's competency in experiment diagnosis, metric and guardrail definition, instrumentation validation, segmentation analysis, and causal reasoning for recommender-system impacts.

  Analytics & ExperimentationOnsite
• PinterestMedium·Data Scientist
  Estimate a Launch Impact with Difference in Differences

  Tests your ability to design and evaluate a regional quasi-experiment using Difference-in-Differences (DiD), validate key identifying assumptions, structure robust inference, and address modern complications such as staggered rollout and network spillovers.

  Analytics & ExperimentationOnsiteDifference-in-DifferencesCausal InferenceQuasi-Experiments
• PinterestMedium·Data Scientist
  Evaluate Carousel and Billboard Lift

  This question tests your ability to design an A/B test for multi-format advertising, specifically handling cannibalization and cross-format lift measurement.

  Analytics & ExperimentationTechnical ScreenA/B TestingCausal InferenceMetrics
• PinterestMedium·Data Scientist
  Evaluate Fresh Content and Video Experiments

  This question evaluates competencies in metric design, experimental design, statistical inference, and product analytics—specifically defining and critiquing freshness metrics, formulating null and alternative hypotheses for content changes, interpreting A/B test results, and calculating sample size and power.

  Analytics & ExperimentationTechnical Screen
• PinterestMedium·Data Scientist
  Evaluate New Feed-Ranking Algorithm with A/B Testing

  Pinterest experimentation prompt on evaluating a new feed-ranking algorithm, covering A/B hypotheses, daily active minutes, guardrails, sample size, MDE, experiment health, mid-test dips, and geo-based causal inference.

  Analytics & ExperimentationOnsite
• PinterestEasy·Data Scientist
  How to evaluate a new Carousel feature

  This question tests your ability to design an A/B test for a feed-based UI feature, focusing on metric selection, counter-metrics, and the identification of potential cannibalization effects.

  Analytics & ExperimentationTechnical ScreenA/B TestingMetricsExperiment Design
• PinterestMedium·Data Scientist
  How would you evaluate a carousel launch?

  This question tests your ability to design a comprehensive evaluation framework for a product feature launch, specifically focusing on metric selection, causal impact measurement, and addressing potential cannibalization effects.

  Analytics & ExperimentationTechnical ScreenExperimentationMetricsProduct Analytics
• PinterestMedium·Data Scientist
  Interpret A/B results for video-pin increase

  This question evaluates statistical inference and experimentation skills, including A/B test hypothesis formulation with a predefined MDE, estimation of absolute and relative lift with confidence intervals, proportion testing for CTR and retention, sample ratio mismatch detection, and multiple-comparison adjustments.

  Analytics & ExperimentationTechnical Screen
• PinterestHard·Data Scientist
  Investigate Homepage Experiment Without Control Group: Methods and Metrics

  Evaluates quasi-experimental analysis and product-metric judgment after a homepage launch lacks a randomized control. Strong answers compare matching and propensity weighting, audit experiment hygiene, choose metrics, and diagnose mixed CTR results.

  Analytics & ExperimentationOnsite
• PinterestMedium·Data Scientist
  Measure Billboard Campaign Impact: Design, Bias, Test Strategy

  This question assesses your ability to design a quasi-experimental framework for measuring the offline impact of an online brand campaign on user acquisition, accounting for external noise and selection bias.

  Analytics & ExperimentationOnsiteCausal InferenceGeo-ExperimentsMetric Design
• PinterestMedium·Data Scientist
  Size an Opportunity and Present It with Clear Visuals

  Tests your ability to size an ambiguous product opportunity using top-down and bottom-up estimation, stress-test core assumptions with sensitivity analysis, and design executive-ready visuals to guide strategic investment decisions.

  Analytics & ExperimentationOnsiteopportunity sizingproduct analyticsdata visualization
• PlaystationMedium·Data Scientist
  Design Experiments Under Network Interference

  This problem tests your understanding of causal inference in networked environments, focusing on how to detect and mitigate bias when treatment effects spill over between connected users in a social gaming platform.

  Analytics & ExperimentationTechnical ScreenCausal InferenceA/B TestingNetwork Science
• QQuoraMedium·Data Scientist
  Test a Post-Creation Tab While Accounting for User Interference

  Design an experiment for a post-creation tab with creator–reader spillovers, cluster-aware analysis and an evidence-based launch decision.

  Analytics & ExperimentationTechnical Screen
• RedditEasy·Data Scientist
  Design A/B test for AI chat box

  This question tests the ability to define metrics, design an A/B testing framework, and account for potential biases when deploying a new AI-powered chat feature.

  Analytics & ExperimentationOnsiteA/B TestingProduct AnalyticsMetrics
• RedditEasy·Data Scientist
  Design experiment for ads in chat with budgets

  This question tests the ability to design an A/B test for a new ad placement in a chat feature while accounting for budget constraints and potential network effects.

  Analytics & ExperimentationOnsiteA/B TestingCausal InferenceExperiment Design
• RedditHard·Data Scientist
  Estimate Incremental Search-Ad Revenue Without an A/B Test

  Design an evaluation framework to estimate the incremental search-ad revenue of a small-scale manual pilot, incorporating variance reduction, inference under heavy-tailed revenue, and ecosystem cannibalization.

  Analytics & ExperimentationOnsiteexperimentationcausal-inferencead-tech
• RedditEasy·Data Scientist
  Measure impact of ads-manager automation feature

  This question tests your ability to design an evaluation framework for a new feature, specifically focusing on causal inference, metric selection, and identifying potential biases in a two-sided marketplace.

  Analytics & ExperimentationOnsiteA/B TestingCausal InferenceMetrics