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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 181–210 of 306 results
- MetaEasyData ScientistDetect bots using comment distribution patterns
This problem tests your ability to define metrics for identifying automated behavior and your statistical intuition for validating those metrics against human behavior.
Analytics & ExperimentationTechnical ScreenAnalyticsAnomaly DetectionFeature Engineering - MetaMediumData ScientistDetermine High-Quality Notifications with CTR Analysis
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 High-Quality Notifications with CTR Analysis states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistDetermine Value of Prioritizing Accounts by Unread Notifications
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 Value of Prioritizing Accounts by Unread Notifications states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistDetermining the optimal ad load in News Feed
Evaluates optimal News Feed ad-load strategy using marginal revenue, user tolerance, and long-term LTV. Strong answers test ad-load levels, segment tolerance, personalize caps, and monitor revenue, retention, and user-experience guardrails.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistDiagnose sales correlations without claiming causality
This question tests the ability to design an observational study that identifies associations between a new feature adoption and sales growth while strictly avoiding causal claims in the presence of self-selection bias.
Analytics & ExperimentationTechnical ScreenObservational StudyCausal InferenceAnalytics - MetaHardData ScientistEvaluate Facebook Dating launch and validate success
This question evaluates a data scientist's competency in product analytics, experimental design, causal inference, and launch validation by requiring definition of primary and guardrail metrics, test unit and spillover considerations, power/duration planning, behavioral signal expectations, quasi-experimental alternatives, and scale/readiness criteria. Commonly asked in Analytics & Experimentation interviews because organizations need assurance a pilot can be scaled safely and reliably, it tests practical application of experimental methods alongside conceptual understanding of statistical power, spillover risk, diagnostic checks, and operational concerns such as privacy and abuse monitoring.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistEvaluate Impact of Increasing Stranger Content in Feeds
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 Impact of Increasing Stranger Content in Feeds states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistEvaluate Notification-Based Account Ranking
This question tests the ability to design an experiment to measure the causal impact of a new notification strategy on account ranking quality and user engagement.
Analytics & ExperimentationTechnical ScreenExperimentationCausal InferenceMetrics - MetaMediumData ScientistEvaluate a Live-Stream Group Notification Under Network Effects
This question tests the ability to design an experiment that accounts for spillover effects and user fatigue in a social graph, while proposing causal inference methods for environments where randomization is restricted.
Analytics & ExperimentationTechnical ScreenCausal InferenceNetwork EffectsExperimentation - MetaHardData ScientistEvaluate shopping tab pre- and post-launch
This question evaluates a data scientist's competency in causal inference, experiment design, funnel metric instrumentation, revenue modeling, sensitivity analysis, and data‑quality triage within the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistEvaluating a 15 % reduction in post‑card height
Evaluates feed UX experimentation for reducing post-card height and diagnosing divergent geographic revenue outcomes. Strong answers measure scroll efficiency, viewability, engagement, ad revenue, device/network effects, and geo-specific decisions.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistEvaluating the Facebook ‘Memory’ feature
Evaluates Facebook Memories feature performance beyond click-through rate and surface-level engagement. Strong answers measure incremental engagement, retention, cannibalization, quality guardrails, frequency caps, sensitive-content handling, and launch criteria.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistEvaluating the Impact of Duplicate and Stolen Posts on a Content Platform
This question tests the ability to design a measurement framework for evaluating content-quality problems on a large user-generated-content platform. It assesses competency in metric selection, experimentation design, and causal reasoning within the Analytics & Experimentation domain — skills central to data science roles focused on platform health.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistHow investigate a brand-ad spend drop?
This question evaluates a data scientist's competency in diagnosing ad-spend anomalies, including data validation, attribution and reporting integrity, segmentation and placement effects, and causal inference within advertising analytics.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistHow to Validate Friends' Content Engagement Hypothesis?
Evaluates how to validate whether friends' content drives stronger social engagement than unconnected content. Strong answers define socialness metrics, control observational bias, test launch impact for unconnected feed content, and monitor discovery, retention, cannibalization, and quality guardrails.
Analytics & ExperimentationTechnical Screen - MetaEasyData ScientistHow to evaluate a similar-listing notifications feature
A Meta Data Scientist analytics-and-experimentation interview question: a US C2C second-hand marketplace is considering opt-in “similar listings” notifications. You must (1) decide whether to build it — hypotheses, opportunity sizing, risks, MVP plan — and (2) design the post-launch measurement: a user-randomized A/B test with a primary/diagnostic/guardrail metric stack, handling of opt-in selection bias, and a clear launch/rollback rule.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistHow to evaluate new listing notifications?
This problem tests your ability to design an A/B test for a marketplace notification feature, focusing on balancing buyer engagement with potential cannibalization and ecosystem health metrics.
Analytics & ExperimentationTechnical ScreenA/B TestingMetricsMarketplace - MetaEasyData ScientistHow would you define and use retention metrics?
This question evaluates a data scientist's competency in product analytics and experimentation by assessing understanding of retention metric definitions, cohorting strategies, short- versus long-term trade-offs, common measurement pitfalls, and A/B test design considerations.
Analytics & ExperimentationTechnical Screen - MetaEasyData ScientistHow would you evaluate pixel-issue notifications?
This question tests the ability to build a comprehensive evaluation framework for a new feature, specifically focusing on defining success metrics, identifying potential side effects, and designing an experimental setup for pixel-issue notifications.
Analytics & ExperimentationTechnical ScreenMetric DesignA/B TestingCausal Inference - MetaEasyData ScientistHow would you evaluate upranking shop ads?
This question tests your ability to design an A/B testing framework for evaluating ad ranking changes, including metric selection, segment analysis, and identifying potential biases in historical data.
Analytics & ExperimentationTechnical ScreenA/B TestingMetricsAdTech - MetaMediumData ScientistHow would you measure Group Call success?
This question evaluates a data scientist's ability to design a measurement framework encompassing product goal definition, north-star metric selection, success metrics for adoption, engagement and quality, precise retention definitions (app-level vs feature-specific and creator vs participant), cohort retention windows, and experimental design including guardrails, bias sources and network effects. It is asked in the Analytics & Experimentation domain to assess practical application of product analytics and quantitative reasoning about short-term versus long-term tradeoffs, segmentation and experimental validity, operating at a primarily practical application level with necessary conceptual understanding.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistIdentify non-table data for feature demand
The question evaluates a data scientist's competency in triangulating product demand from non-table sources, defining and de-biasing signals, specifying metrics and guardrails, and designing evidence-driven experiments within the Analytics & Experimentation domain for a Data Scientist role.
Analytics & ExperimentationTechnical Screen - MetaEasyData ScientistInvestigate why an advertiser’s spend decreased
This question tests your ability to systematically isolate the root cause of a drop in advertising spend by decomposing metrics and evaluating external versus internal factors.
Analytics & ExperimentationTechnical ScreenRoot Cause AnalysisMetrics DecompositionAdTech - MetaMediumData EngineerMeasure Game Monetization and Validate a Reporting Repair
Define gaming monetization metrics, separate business changes from data failures, and validate repairs with reconciliation and guardrails.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistMeasure Harmful Content Impact with Key Metrics
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 Measure Harmful Content Impact with Key Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistMeasure and mitigate notification spam
This question tests the ability to design an experimentation framework for notification systems while accounting for user fatigue, long-term ecosystem health, and signal interference.
Analytics & ExperimentationTechnical ScreenExperimentationProduct MetricsCausal Inference - MetaHardData ScientistProve friends outperform unconnected; design metrics, observational analysis, and rollout experiment
A Meta data-scientist technical-screen question on Analytics & Experimentation: using only info_stream_views and post_reactions, prove whether Friend-authored content is 'more social' than Unconnected content. It tests denominator-complete metric design with relationship attribution, observational causal validation (fixed effects + propensity/AIPW), and a network-aware rollout experiment with power, CUPED, interference mitigations, and guardrails — plus quantifying the long-term and discovery value of Unconnected content beyond near-term engagement.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistProve high-quality pixels improve ad performance
This question tests your ability to design a rigorous causal experiment to validate a product hypothesis while accounting for selection bias, confounding variables, and operational constraints.
Analytics & ExperimentationTechnical ScreenCausal InferenceExperiment DesignMetric Strategy - MetaMediumData ScientistSelect and prioritize metrics with guardrails
This question evaluates a candidate's ability to design a robust metrics framework for product experiments, covering selection and normalization of success metrics, feature-only sizing and ecosystem metrics, explicit cannibalization reads, guardrails, multiple-testing control, and back-of-the-envelope LTV/ARPDAU impact estimation.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistShould We Launch Group Calling?
A Meta Data Scientist technical-screen case: using only one-to-one call logs, decide whether to build a group calling feature and how to measure a launch's success. It tests product analytics, proxy-metric design, network analysis, causal inference, and experiment design under social-network interference.
Analytics & ExperimentationTechnical Screen
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