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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 91–120 of 218 results
- InstacartHardData ScientistAnalyze A/B test with revenue–cost tradeoffs
This question evaluates a data scientist's skills in experimental design and statistical inference for A/B testing, revenue-impact modeling with skewed and zero-inflated metrics, segmentation for heterogeneous treatment effects, and constrained optimization to balance contribution and operational risk.
Analytics & ExperimentationOnsite - InstacartHardData ScientistCompute duration and stopping rules correctly
This question evaluates a data scientist's competency in experimental design and applied statistics, specifically power and sample‑size calculations, accounting for intra‑household clustering and design effects, sequential monitoring for peeking control, and variance‑reduction or stratified randomization to handle day‑of‑week seasonality.
Analytics & ExperimentationOnsite - InstacartHardData ScientistDesign and Interpret an A/B Test
This question tests the ability to design a statistically rigorous A/B test, calculate sample sizes based on effect thresholds, and account for real-world constraints like seasonality and multiple comparisons.
Analytics & ExperimentationOnsiteA/B TestingPower AnalysisStatistics - IntuitEasyProduct AnalystExtract insights from a multi-entry funnel scorecard
This question evaluates a candidate's ability to analyze multistep funnel metrics, handle highly skewed traffic across many access points, and prioritize insights from sparse or long-tail data, testing competencies in product analytics and measurement.
Analytics & ExperimentationOnsite - LinkedInHardData ScientistDesign Experiments for Email Campaign & Messaging Update
Evaluates experiment design for concurrent email and in-product messaging launches with possible interaction effects. Strong answers use factorial designs, power calculations, interaction terms, sequential testing, and guardrails.
Analytics & ExperimentationOnsite - LinkedInMediumData ScientistDiagnose a Drop in Applications from Job Recommendation Emails
Diagnose falling applications from job emails by tracing funnel rates, recommendation relevance, promoted-job mix, and ranking changes.
Analytics & ExperimentationOnsite - LinkedInMediumData ScientistEstimate Redesign Impact Using Propensity Score Matching
Estimate Redesign Impact Using Propensity Score Matching 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 - LinkedInMediumData ScientistImprove Profile Completion Rate
Evaluates analytics and experimentation strategy for improving LinkedIn profile completion. Strong answers define value-weighted completion metrics, segment gaps, prioritize interventions, and test impact with guardrails.
Analytics & ExperimentationOnsite - LinkedInMediumData ScientistMeasure Success of New B2B Product
Evaluates success measurement for a new LinkedIn B2B SaaS product at account and seat levels. Strong answers define activation, engagement, value, retention, expansion, anomaly analysis, and growth prioritization.
Analytics & ExperimentationOnsite - LinkedInMediumData ScientistPresent Insights from Course Supply and Participation Data
Analyze course catalog supply and learner participation using time trends, category comparisons, utilization measures, and evidence-based presentation.
Analytics & ExperimentationOnsite - LinkedInMediumData ScientistSingle Queue vs Multiple Queues — Service Design
Evaluates queueing-system reasoning for single-line versus multiple-line bank teller designs. Strong answers compare M/M/c pooling with parallel M/M/1 queues, waiting-time averages, variance, stability, fairness, and operations.
Analytics & ExperimentationOnsite - LinkedInMediumData ScientistTest a New Feature at Its Natural Product Placement
Design a feature launch experiment that separates natural-placement availability, discoverability, promotional exposure, and downstream product value.
Analytics & ExperimentationOnsite - LyftMediumData ScientistDesign experiments for marketplace balance
This question evaluates a data scientist's competency in experimental design, causal inference, metric definition, statistical power calculation, and operational rollout planning for two-sided marketplaces, and it is commonly asked to assess the ability to account for interference, supply constraints, spillovers, and contamination when measuring treatment effects. Categorized under Analytics & Experimentation, it tests both conceptual understanding and practical application by probing exposure and eligibility definitions, metric and guardrail specification, sample-size and clustering adjustments, recognition of heterogeneous treatment effects and sequential monitoring concerns, variance-reduction considerations, and rollout and fallback decision-making.
Analytics & ExperimentationOnsite - 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 - 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 - 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 - 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 - 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 - 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 a feed ads A/B test with guardrails
This question evaluates a data scientist's competency in experimental design, causal inference, statistical power analysis, and product-metric trade-offs within the Analytics & Experimentation domain, and is commonly asked to assess the ability to increase monetization while protecting user and creator experience.
Analytics & ExperimentationOnsite
Instacart
Intuit
LinkedIn
Lyft
Meta