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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
Questions
546
Companies
11
Topics
8
Languages

Showing 61–90 of 560 results

  • BetterHelpHardData Scientist
    Optimize Experiment Thresholds for Impactful Feature Launches

    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 Optimize Experiment Thresholds for Impactful Feature Launches states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    Analytics & ExperimentationTechnical Screen
  • Boston Consulting GroupMediumData Scientist
    Identify Causes and Solutions for Fashion Profit Decline

    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 Identify Causes and Solutions for Fashion Profit Decline states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    Analytics & ExperimentationTechnical Screen
  • ByteDanceMediumData Scientist
    Diagnose Low CTR in an Advertising Campaign Funnel

    Prepare for a product analytics interview by diagnosing below-average advertising CTR from limited campaign-level funnel metrics. The question tests metric interpretation, uncertainty, evidence-versus-hypothesis discipline, and how to plan useful follow-up analysis.

    Analytics & ExperimentationTechnical Screen
  • CVS HealthHardData Scientist
    Design a flu-shot A/B/n campaign experiment

    This question evaluates a data scientist's competency in experimental design, causal inference, sample-size and power calculations, compliance-adjusted effect estimation, attribution instrumentation, and reporting for multi-channel marketing experiments.

    Analytics & ExperimentationTechnical Screen
  • CVS HealthHardData Scientist
    Launch and measure a TV campaign

    This question evaluates a data scientist's competency in experimental design and causal inference, including geo-level matching, KPI definition, media measurement (GRP/TRP, reach/frequency, adstock and saturation modeling), power analysis, and operational risk management for measuring incremental vaccinations.

    Analytics & ExperimentationTechnical Screen
  • Capital OneMediumData Scientist
    Analyze Subscription, Insurance, App, and Card Cases

    This question evaluates product analytics competencies—unit-economics, cohort churn modeling, LTV/CAC and funnel math—requiring clear assumptions, metric definitions, governing formulas, and numeric break-even and conversion calculations.

    Analytics & ExperimentationOnline Assessment
  • Capital OneMediumData Scientist
    Analyze ad watch-time with Excel pivots

    This question evaluates a data scientist's proficiency with Excel PivotTables, date and ISO-week handling, aggregation and ratio metrics (clicks per watch-minute), and deterministic tie-breaking logic for reproducible results.

    Analytics & ExperimentationTechnical Screen
  • Capital OneMediumData Scientist
    Analyze your favorite app and improve it

    This question evaluates a data scientist's competencies in product analytics, monetization analysis, competitive positioning, metric specification (north-star and guardrails), and experimental design including power and validity considerations.

    Analytics & ExperimentationOnsite
  • Capital OneMediumData Scientist
    Assess Customer Value with Varied Contract Terms and Costs

    Evaluates subscription contract unit economics across free periods, service costs, marketing costs, churn penalties, and fixed overhead. Strong answers compute customer value, portfolio value, and break-even acquisitions.

    Analytics & ExperimentationOnsite
  • Capital OneHardData Scientist
    Build a causal ML pipeline end-to-end

    This question evaluates expertise in causal inference and policy learning, covering ATE/CATE estimation, heterogeneous treatment effect methods, off‑policy evaluation, overlap and sensitivity diagnostics, and operational deployment with fairness guardrails in the Analytics & Experimentation domain.

    Analytics & ExperimentationOnsite
  • Capital OneMediumData Scientist
    Calculate Profit and Analyze Vegan Burger Market Trends

    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 Calculate Profit and Analyze Vegan Burger Market Trends states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    Analytics & ExperimentationTechnical Screen
  • Capital OneEasyData Scientist
    Calculate Profitability and Break-Even for Lyft Partnership Campaign

    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 Calculate Profitability and Break-Even for Lyft Partnership Campaign states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    Analytics & ExperimentationHR Screen
  • Capital OneHardData Scientist
    Challenge and validate assumptions

    This question evaluates a data scientist's competency in identifying fragile business assumptions, conducting sensitivity analysis, and designing experiments and observational studies to validate parameters related to demand, price elasticity, cannibalization, supply constraints, labor learning curves, regional heterogeneity, and promotional decay.

    Analytics & ExperimentationTechnical Screen
  • Capital OneMediumData Analyst
    Compare Two-Year Economics for Renewing or Replacing a Show

    Calculate two-year show profits, derive the success-probability break-even point, and evaluate renewal and profit-improvement choices.

    Analytics & ExperimentationTechnical Screen
  • Capital OneMediumData Scientist
    Compute capacity, staffing trade-offs, and break-even

    This question tests the ability to model operational costs, evaluate staffing trade-offs between overtime and contracting, and perform break-even analysis under varying volume assumptions.

    Analytics & ExperimentationHR ScreenCapacity PlanningCost AnalysisOperations
  • Capital OneMediumData Scientist
    Compute partnership profit and break-even population

    This question evaluates competency in analytics and experiment-driven financial modeling, covering unit economics, cannibalization versus true lift, break-even and sensitivity analysis, and interpretation of segment-level impacts on partnership profitability.

    Analytics & ExperimentationTechnical Screen
  • Capital OneMediumData Scientist
    Decide Which Show to Renew

    This problem tests the ability to calculate expected value and ROI for a business decision under uncertainty, requiring the candidate to incorporate subscriber churn risk and lifetime value metrics.

    Analytics & ExperimentationTechnical ScreenExpected ValueROIChurn Analysis
  • Capital OneMediumData Scientist
    Decide and test Groupon program incrementality

    This question evaluates experimental design, causal inference, uplift and profitability modeling, operational risk assessment, and KPI-driven decision rules within the Analytics & Experimentation domain for data scientists, testing both conceptual understanding and practical application of A/B testing, sample-size considerations, and guardrail metrics. It is commonly asked to determine whether a candidate can translate business constraints into a rigorous experiment and profit-oriented analysis, balancing incremental impact estimation, cannibalization concerns, and operational trade-offs to support go/no-go decisions.

    Analytics & ExperimentationTechnical Screen
  • Capital OneMediumData Scientist
    Decide content volume and price under uncertainty

    This question evaluates a candidate's competency in pricing and content-production optimization under uncertainty, including defining optimization objectives and constraints, specifying necessary metrics, and assessing sensitivity to changes in variable costs.

    Analytics & ExperimentationHR Screen
  • Capital OneHardData Scientist
    Decide on vegan-burger R&D investment

    This question evaluates quantitative business-case construction, including market sizing (TAM/SAM/SOM), unit economics and financial modeling, risk assessment and option valuation, plus experimental-design skills within the Analytics & Experimentation domain for a Data Scientist role.

    Analytics & ExperimentationTechnical Screen
  • Capital OneMediumData Scientist
    Decide whether to sell both SKUs

    This question evaluates a data scientist's competency in profit-impact modeling, break-even analysis, and accounting for cannibalization effects using unit contribution margins and fixed costs.

    Analytics & ExperimentationTechnical Screen
  • Capital OneHardData Scientist
    Define and validate an airline profitability metric

    This question evaluates competency in metric design, business analytics, causal inference, and experimental design by requiring a decomposable airline route profitability metric, operational guardrails, validation via backtests and sensitivity analyses, and a policy experiment plan.

    Analytics & ExperimentationOnsite
  • Capital OneMediumData Scientist
    Design A/B Test for Marketing Campaign Impact Evaluation

    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 A/B Test for Marketing Campaign Impact Evaluation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    Analytics & ExperimentationOnsite
  • Capital OneHardData Scientist
    Design A/B test for credit card offer

    This question evaluates a data scientist's competency in experimental design, causal inference, metric definition and measurement windows, power analysis, bias mitigation, identity resolution, and regulatory compliance for financial-product A/B testing.

    Analytics & ExperimentationTechnical Screen
  • Capital OneMediumData Scientist
    Design a better water bottle and test it

    This question evaluates experimental design, product analytics, hypothesis formulation, metric selection, sample size/power/MDE estimation, segmentation, and risk and rollback planning within the Analytics & Experimentation domain for Data Scientist roles.

    Analytics & ExperimentationOnsite
  • Capital OneHardData Scientist
    Design a profit growth strategy

    This question evaluates a data scientist's competency in product analytics, marketplace economics, pricing strategy, demand elasticity estimation, experimentation, and quantitative impact sizing.

    Analytics & ExperimentationTechnical Screen
  • Capital OneHardData Scientist
    Design and analyze a card signup A/B test

    This question evaluates a data scientist's competency in A/B test design, causal inference, statistical power and sample-size calculation, risk-adjusted profit metrics, sequential testing, identity resolution, and monitoring for credit and fraud impacts.

    Analytics & ExperimentationTechnical Screen
  • Capital OneHardData Scientist
    Design and analyze ad A/B test

    This question evaluates a candidate's competency in online experimentation and statistical analysis, covering hypothesis formulation, variance reduction, clustering and stratification, multiplicity control, power/MDE calculation, and operational metric guardrails.

    Analytics & ExperimentationOnline Assessment
  • Capital OneHardData Scientist
    Design and analyze ads A/B test this week

    This question evaluates experimental design and applied statistical analysis skills for A/B testing, covering randomization, exposure control, stratification, variance reduction methods, metric formalization, hypothesis testing, sample sizing, sequential monitoring, diagnostics, and handling delayed offline conversions.

    Analytics & ExperimentationOnline Assessment
  • Capital OneHardData Scientist
    Design and analyze an SBA mini case experiment

    This question evaluates expertise in experimental design and causal inference, including sample-size and power calculations, ITT/TOT analysis, variance reduction techniques, heterogeneity measurement, data quality and fraud handling, guardrail monitoring, and rollout decision-making for product experiments.

    Analytics & ExperimentationTechnical Screen