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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.
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Showing 421–450 of 560 results
- OtherHardData ScientistDefine goals and success metrics for subscriber-only features
This question evaluates a data scientist's competency in product analytics and experimentation, specifically goal selection, success metric definition, ROI calculation, cannibalization analysis, and end-to-end experiment and instrumentation design for subscriber-only features.
Analytics & ExperimentationTechnical Screen - OtherHardData ScientistDesign an A/B test with guardrails
This question evaluates a data scientist's skills in experimental design, hypothesis testing, statistical power and sample-size calculation, randomization and contamination control, pre-registration planning, and multiplicity adjustment for A/B testing.
Analytics & ExperimentationOnsite - OtherHardData ScientistDesign metrics and experiment
This question evaluates a candidate's competency in experimental design, success-metric definition, event instrumentation, causal inference for heterogeneous treatment effects, and statistical power and sample-size calculations for subscription-product analytics.
Analytics & ExperimentationTechnical Screen - OtherMediumData ScientistDesign metrics resilient to data quality
This question evaluates the ability to design robust data-quality metrics and apply SQL window-function techniques for deduplication and time-windowed completeness calculations, focusing on quantifying how ingestion delays, duplicates, and negative amounts can bias payment metrics.
Analytics & ExperimentationTechnical Screen - OtherEasyData ScientistHow would you measure and experiment on harmful content?
This question evaluates a data scientist's competency in measuring and experimenting on harmful user-generated content, focusing on severity quantification, metric selection, and randomized experiment design within the analytics and experimentation domain.
Analytics & ExperimentationTechnical Screen - OtherMediumData ScientistSeparate demand from supply for jeans
This question evaluates a data scientist's ability to perform data-driven diagnostics, funnel-metric construction, quantitative decision-rule design, and experimentation planning to distinguish demand-side from supply-side constraints in a marketplace jeans category.
Analytics & ExperimentationOnsite - PayPalMediumData ScientistAnalyze Transactions for Risk and Implement Mitigation Strategies
Analyze Transactions for Risk and Implement Mitigation Strategies 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 - PayPalHardData ScientistAssess card transactions and plan risk strategy
Assess card transactions and plan risk strategy 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 - PayPalMediumData ScientistBoost User Login Rate: Key Metrics to Monitor
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 Boost User Login Rate: Key Metrics to Monitor states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - PayPalHardData ScientistDesign 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 - PayPalMediumData ScientistDesign 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 - PayPalEasyData ScientistDesign 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 - PayPalEasyData ScientistDesign 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 - PayPalEasyData ScientistHow 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 - PayPalMediumData ScientistMaster 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 - PayPalEasyData ScientistReduce 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 - PinterestHardData ScientistAnalyze 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 - PinterestHardData ScientistDecide 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 - PinterestMediumData ScientistDesign 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 - PinterestHardData ScientistDesign 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 - PinterestMediumData ScientistDesign 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 - PinterestHardData ScientistDesign 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 - PinterestHardData ScientistDiagnose 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 - PinterestMediumData ScientistEstimate 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 - PinterestMediumData ScientistEvaluate 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 - PinterestMediumData ScientistEvaluate 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 - PinterestMediumData ScientistEvaluate 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 - PinterestEasyData ScientistHow 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 - PinterestMediumData ScientistHow 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 - PinterestMediumData ScientistInterpret 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
PayPal
Pinterest