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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.
- 9403
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- 546
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- 11
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- 8
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Showing 271–300 of 560 results
- LyftHardData ScientistHow to target commute coupon users?
This question tests your ability to design an experiment to measure the incremental lift of a coupon program and your strategy for segmenting users based on commuting behaviors to maximize ROI.
Analytics & ExperimentationTechnical ScreenCausal InferenceExperimentationUser Segmentation - LyftMediumData ScientistInvestigate Causes of Driver WOW Score Drop
Investigate Causes of Driver WOW Score Drop 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 - LyftMediumData ScientistInvestigate Metric Drops and Coupon Retention
This question tests your ability to diagnose a sudden drop in a core business metric, distinguish between technical tracking issues and product-driven behavior, and design a strategy to evaluate coupon effectiveness.
Analytics & ExperimentationTechnical ScreenProduct AnalyticsExperimentationCausal Inference - LyftMediumData ScientistInvestigate Sudden Metric Changes and Design A/B Test
Investigate Sudden Metric Changes and Design A/B Test 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 & ExperimentationTechnical Screen - LyftMediumData ScientistInvestigate a 7% Monthly Active Riders Drop and a 20% Wait-Time Increase
Tests your ability to conduct a structured root-cause analysis on simultaneous marketplace metric regressions, decomposing supply-demand dynamics and separating correlation from causation.
Analytics & ExperimentationTechnical ScreenMarketplace AnalyticsRoot Cause AnalysisMetrics & KPIs - MetaHardData ScientistAnalyze Algorithm's Impact on Diverse Demographics and Validate Causes
Analyze Algorithm's Impact on Diverse Demographics and Validate Causes 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 - MetaHardData ScientistAnalyze Change in App Metrics and Feature Impact
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 Analyze Change in App Metrics and Feature Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistAnalyze Comment Distribution Using Statistical Metrics and Tests
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 Analyze Comment Distribution Using Statistical Metrics and Tests states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistAnalyze Key Metrics for Notification System Success
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 Analyze Key Metrics for Notification System Success states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistAnalyze Revenue Shifts to Identify Cannibalization Effects
Evaluates revenue cannibalization analysis across creation sources. Strong answers define Source A, other-source revenue, total revenue, cannibalization ratio, and net lift, then use panel, rollout, difference-in-differences, or synthetic-control evidence to separate source shifting from organic growth.
Analytics & ExperimentationOnsite - MetaMediumData ScientistAnalyzing abuse in the content‑reporting system
Evaluates reporting-system analytics for valid reports and misuse detection in moderation workflows. Strong answers define distinct reported-user share, write SQL for valid reports, and detect abusive reporters through frequency, hit-rate, burst, and coordination metrics.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistAssess ranking change and design experiment
This question evaluates a candidate's competency in causal inference, experimentation design, metric definition, and observational analytics for product ranking and engagement problems within the Analytics & Experimentation domain for Data Scientist roles.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistAssessing whether a new metric A is meaningful for News Feed
Evaluates whether a proposed News Feed proxy metric is meaningful for long-term user value. Strong answers test predictive power, actionability, gaming risk, true-outcome alignment, collection cost, privacy, latency, and decision criteria.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistBrainstorm how to optimize email engagement
This question evaluates a data scientist's competency in lifecycle email optimization, attribution of incremental on-site engagement, experiment design, metric selection, and trade-offs involving deliverability and guardrails within the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistBuild dashboard; diagnose engagement–purchase gap
This question evaluates a data scientist's competency in product analytics and experimentation, focusing on dashboard specification, precise event- and user-level metric definitions, core dimensions, data freshness SLAs, and instrumentation within the Analytics & Experimentation domain.
Analytics & ExperimentationOnsite - MetaHardData ScientistChoose alternatives when randomization fails
This question evaluates proficiency in causal inference and experimentation analytics for a Data Scientist role, focusing on selecting appropriate identification strategies, designing placebo and pre‑trend tests, and specifying robustness and sensitivity analyses for non‑randomized feature adoption.
Analytics & ExperimentationOnsite - MetaHardData ScientistChoose group-call size cap via experiment
This question evaluates a candidate's competency in experimental design, A/B testing and statistical analysis for product decisions, specifically testing metric selection, reliability and latency guardrails, stratified randomization, interference and clustering effects, heterogeneity detection, and operational stopping rules.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistCompare Instagram and Facebook Stories Using Key Performance 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 Compare Instagram and Facebook Stories Using Key Performance Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistCompare Instagram vs. Facebook using causal experiments
This question evaluates causal inference, experimentation design, metric engineering, and ecosystem-level impact assessment skills for a Data Scientist in the Analytics & Experimentation domain, with emphasis on defining a single-objective OEC that prevents pure cannibalization and on measuring cross-app effects via randomized rollouts.
Analytics & ExperimentationOnsite - MetaMediumData ScientistDecide and experiment on Group Call feature
This question evaluates a data scientist's proficiency in product analytics, causal inference, metric engineering, experimentation design, and post‑launch diagnostics within the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistDecide event notification launch via experiments
This question tests the ability to design a robust A/B test in the presence of network effects, interference, and cannibalization within a social notification ecosystem.
Analytics & ExperimentationTechnical ScreenA/B TestingNetwork EffectsMetrics - MetaHardData ScientistDecide when CTR falls but revenue rises
This question evaluates understanding of A/B experimentation, auction-based ad metrics, metric decomposition for revenue impact, causal diagnostics for hidden heterogeneity (e.g., Simpson’s paradox), and executive-level results presentation within the Analytics & Experimentation domain.
Analytics & ExperimentationOnsite - MetaMediumData ScientistDefine Success for a New Group Feature Without Hiding Cannibalization
Design a comprehensive measurement and experimentation framework for a new community feature, evaluating true incremental value while detecting cannibalization across existing surfaces and mitigating network spillover effects.
Analytics & ExperimentationTechnical Screenexperimentationmetric-designcannibalization - MetaHardData EngineerDefine and validate product metrics
Define and validate product metrics 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 - MetaEasyData ScientistDefine engagement metrics and analyze comment distribution
This question evaluates a data scientist's competency in metric framework design, distributional analysis of heavy-tailed user behavior, monitoring and anomaly detection, and experimental setup for measuring healthy engagement, including considerations like unit of analysis, treatment of heavy users, and anti-spam guardrails.
Analytics & ExperimentationOnsite - MetaHardData ScientistDesign A/B test and success metrics for new feature
This question evaluates a data scientist's competency in experimental design, metric selection and guardrails, power and sample-size calculations, logging and attribution specifications, and revenue impact estimation for a new feature on a social media platform.
Analytics & ExperimentationOnsite - MetaMediumData ScientistDesign B2C chatbot success metrics and test plan
This question evaluates a data scientist's ability to design product success metrics and a rigorous experiment plan, covering KPI formulation, guardrail metrics and thresholds, randomization and experiment design, logging instrumentation, and quasi-experimental causal inference.
Analytics & ExperimentationOnsite - MetaHardData ScientistDesign Experiment to Measure Shopping Feature Impact
Meta data scientist experimentation prompt on measuring Instagram Shopping impact with A/B tests, geo or cluster holdouts, engagement and revenue metrics, guardrails, selection bias, network effects, and rollout decisions.
Analytics & ExperimentationOnsite - MetaHardData ScientistDesign Messenger spam experiment with clustering
This question evaluates a data scientist's skills in experimental design, causal inference, clustering and interference analysis, metric specification, and power/sample-size calculation within the Analytics & Experimentation domain for Data Scientist roles.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistDesign Metrics to Track and Analyze Spam Impact
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 Metrics to Track and Analyze Spam Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen
Lyft
Meta