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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 121–150 of 218 results
- MetaHardData ScientistDesign a small-sample launch experiment in Europe
This question evaluates a data scientist's competencies in experimental design, causal inference, estimand specification, power and MDE calculations for heavy-tailed and overdispersed outcomes, interference handling, compliance/IV considerations, and sequential monitoring in field experiments.
Analytics & ExperimentationOnsite - MetaHardData ScientistDesign an Experiment to Evaluate New ML Model
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 an Experiment to Evaluate New ML Model states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaHardData ScientistDesign an Experiment to Evaluate New Recommendation Model
This problem tests the ability to design an end-to-end A/B test framework for a machine learning model update, focusing on metric selection, causal validity, and post-experiment analysis.
Analytics & ExperimentationOnsiteA/B TestingExperimentationMetric Design - MetaHardData ScientistDesign and evaluate P2P payments in messaging
This question evaluates a data scientist's competency in product analytics, causal inference, experimental design under network interference, monetization measurement (ARPU impact), and risk mitigation for a peer-to-peer payments feature relevant to a Data Scientist role.
Analytics & ExperimentationOnsite - MetaMediumData ScientistDesign and validate ad model launch
This question evaluates competence in experimental design, causal inference, online A/B testing and launch decision frameworks for ad recommendation systems, covering randomization/unit choice, metric definition and guardrails, power and duration estimation, validity checks, monitoring and visualization in the Analytics & Experimentation domain.
Analytics & ExperimentationOnsite - MetaHardData ScientistDesign and validate an ads feed experiment
This question tests the ability to design a rigorous A/B test for a core product feature, covering metric selection, interference handling, and post-experiment analysis under complex constraints.
Analytics & ExperimentationOnsiteA/B TestingCausal InferenceProduct Metrics - MetaHardData ScientistDesign experiments under network interference
This question evaluates a data scientist's competence in experimental design and causal inference under network interference, assessing understanding of clustering strategies, exposure and exclusion definitions, contamination control, design effect and sample-size implications, estimation of intra-cluster correlation, and mixed-effects analysis for evolving clusters. Commonly asked in Analytics & Experimentation interviews to probe the ability to preserve internal validity while managing operational feasibility in two-sided marketplaces, it tests both conceptual understanding of interference and practical application skills in design and analysis planning.
Analytics & ExperimentationOnsite - MetaEasyData ScientistDesign metrics to detect harmful content and fraud
This problem tests the ability to design robust monitoring metrics for detecting fraudulent user activity and policy-violating content, focusing on trade-offs between precision and recall.
Analytics & ExperimentationOnsiteAnalyticsMetricsFraud-Detection - MetaMediumData ScientistDetect and address Simpson’s paradox
This question evaluates understanding of aggregation bias and Simpson's paradox, proficiency with stratified or mixed-model estimators, heterogeneity testing, and experiment decisioning within Analytics & Experimentation for a Data Scientist role.
Analytics & ExperimentationOnsite - MetaEasyAnalytics EngineerDetect fake accounts and measure their impact
This question tests the ability to design an analytical framework for identifying fraudulent behavior and quantifying the financial or engagement impact of those accounts on a platform.
Analytics & ExperimentationOnsiteFraud DetectionCausal InferenceMetrics - MetaHardData ScientistDetermine Key Metrics and Design A/B Test for Ad Ranking
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 Key Metrics and Design A/B Test for Ad Ranking states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistDetermine Metrics to Evaluate Notification Impact on Users
Evaluates product analytics judgment for Facebook push notifications, including how to measure helpful versus harmful notifications, design an event-notification experiment, and balance short-term clicks against retention, fatigue, and opt-out guardrails. Strong answers build a metric hierarchy and use causal measurement.
Analytics & ExperimentationOnsite - MetaHardData ScientistEstimate revenue of organic shopping tab
This question evaluates a data scientist's skills in revenue modeling, funnel construction, causal inference for bias correction, experimentation scaling, and sensitivity analysis using organic engagement and purchase metrics.
Analytics & ExperimentationOnsite - MetaMediumData ScientistEvaluate AI-assisted ad creation
This question evaluates competency in product analytics, experimentation design, causal inference, metric definition, and measurement of downstream business and user-quality impacts for an AI-assisted advertising feature.
Analytics & ExperimentationOnsite - MetaEasyData ScientistEvaluate AI-assisted ads creation feature
This question assesses the ability to design an A/B test for a new AI feature, focusing on metric definition, experiment design, and mitigating potential trade-offs between advertiser efficiency and ad quality.
Analytics & ExperimentationOnsiteA/B TestingProduct MetricsExperiment Design - MetaMediumData ScientistEvaluate Instagram's Short-Video Recommender System Success
Evaluates success metrics and experimentation for Instagram's short-video recommender feed. Strong answers choose a durable user-value metric, understand heavy-tailed engagement distributions, handle metric trade-offs, and outline a safe A/B test with retention, safety, creator, and app-level guardrails.
Analytics & ExperimentationOnsite - MetaHardData ScientistEvaluate Messenger's P2P Payments Feature for Business Viability
Evaluates business viability for a Messenger P2P payments feature. Strong answers define networked adoption and transfer metrics, assess fraud, compliance, support, trust, and unit economics, and propose a beta or experiment design that handles spillovers and contamination.
Analytics & ExperimentationOnsite - MetaHardData ScientistEvaluate New Ad Model with A/B Testing Experiment
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 Evaluate New Ad Model with A/B Testing Experiment states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistEvaluate a New Ads-Ranking Algorithm
This question tests the ability to design a robust A/B test for an ads-ranking system while accounting for interference effects, budget constraints, and conflicting metric signals.
Analytics & ExperimentationOnsiteA/B TestingAd-TechMetrics - MetaMediumData ScientistEvaluate fake accounts and ad creation
This question tests the ability to design an evaluation framework for identifying malicious actors in an ad-tech environment and measuring the performance of automated content generation tools.
Analytics & ExperimentationOnsiteAnalyticsExperimentationMetric Design - MetaHardData ScientistEvaluate the Health of Facebook Groups
Evaluates Facebook Group health metrics and threaded-comments experiment design. Strong answers define normalized health scores, compare small and large groups fairly, and test deep interaction with safety guardrails.
Analytics & ExperimentationOnsite - MetaEasyData ScientistExplain why IG Story usage exceeds Facebook
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 Explain why IG Story usage exceeds Facebook states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumProduct AnalystHow would you drive product growth?
This question evaluates product growth analytics competencies including experimentation design, funnel decomposition, metric definition and guardrails, segmentation, and causal inference within the Analytics & Experimentation domain for social media and mobile product features.
Analytics & ExperimentationOnsite - MetaEasyData ScientistHow would you evaluate emoji reactions launch?
This question tests the ability to design an experimentation framework for a new feature in a social product, specifically focusing on metric selection, identifying interference, and isolating the causal impact of a feature rollout.
Analytics & ExperimentationOnsiteProduct MetricsA/B TestingCausal Inference - MetaEasyProduct AnalystHow would you grow key product metrics?
This question assesses your ability to systematically approach product growth by identifying bottlenecks, framing hypotheses, and designing experiments to improve key user engagement metrics.
Analytics & ExperimentationOnsiteMetricsA/B TestingFunnel Analysis - MetaMediumData ScientistInterpreting confidence intervals to choose a treatment
Evaluates confidence-interval interpretation for choosing among feed-ranking treatments with engagement losses. Strong answers compare statistical significance, magnitude, uncertainty, business thresholds, downside risk, and launch recommendations.
Analytics & ExperimentationOnsite - MetaHardData ScientistInvestigate Reasons for Higher Instagram Story Consumption
Evaluates diagnostic analytics for explaining why Instagram users consume more Stories than Facebook users. Strong answers validate metric parity, decompose supply and demand drivers, use cohort and within-user comparisons, and propose experiments for UI, ranking, content, or notification hypotheses.
Analytics & ExperimentationOnsite - MetaHardData ScientistJustify building a new feature with evidence
This question evaluates a data scientist's product analytics, experimental design, and strategic decision-making skills, covering market sizing, competitive analysis, privacy-aware data and infrastructure assessment, risk identification, and A/B testing planning.
Analytics & ExperimentationOnsite - MetaMediumData ScientistMeasure a friend-recommendation launch
This question evaluates experiment design, metric selection, causal inference and business-metric translation for social features, including defining primary and guardrail metrics, choosing experimental units to mitigate network interference, and estimating revenue impact from observed lifts.
Analytics & ExperimentationOnsite - MetaHardData ScientistMeasure impact of bot mitigation via experiment
This question evaluates experiment design and causal inference skills for platform analytics, covering interference management, metric and guardrail definition, power and duration calculations, eligibility and exclusions, novelty/adaptation considerations, and diagnostics for bot‑mitigation interventions.
Analytics & ExperimentationOnsite
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