Data Scientist Interview Questions

Showing 211–240 of 513 results

• HHuluMedium·Data Scientist
  Design and Monitor a Multi-Country Parameter Experiment

  This question tests the ability to design an A/B test across multiple regions while accounting for sample size requirements, variance reduction techniques, and sequential monitoring protocols.

  Analytics & ExperimentationTechnical ScreenA/B TestingPower AnalysisCUPED
• HHuluMedium·Data Scientist
  Interpret a Streaming Regression Without Overstating Causality

  This problem tests your ability to interpret multivariate regression results for product stakeholders, specifically focusing on distinguishing correlation from causation and identifying risks like collider bias.

  Analytics & ExperimentationTechnical ScreenRegressionCausal InferenceExperimental Design
• IBMEasy·Data Scientist
  Estimate free-trial conversion probability

  This question evaluates a data scientist's competency in cohort definition, censored-data handling, segmentation and confounding assessment, and uncertainty quantification for measuring free-trial to paid conversion, and it belongs to the Analytics & Experimentation domain within data science.

  Analytics & ExperimentationTechnical Screen
• InstacartHard·Data Scientist
  Analyze 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
• InstacartHard·Data Scientist
  Compute 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
• InstacartEasy·Data Scientist
  Design a pricing experiment with network effects

  This question evaluates experimental design and causal inference competencies for a two-sided marketplace pricing change, including handling network effects/interference, choice of unit of randomization and treatment markets, metric selection and diagnostics, power/MDE estimation, and operational risk assessment within the Analytics & Experimentation domain for a Data Scientist role. It is commonly asked to assess practical application of randomized and quasi-experimental methods across heterogeneous geographies and operational constraints, requiring both hands-on experimental design skills and conceptual understanding of bias, spillovers, and diagnostic metrics.

  Analytics & ExperimentationTechnical Screen
• InstacartHard·Data Scientist
  Design 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
• InstacartMedium·Data Scientist
  Diagnose and fix low conversion rigorously

  This question evaluates a Data Scientist's competency in analytics and experimentation, covering instrumentation validation, funnel and segmentation analysis, causal inference methods (holdouts/DiD), system reliability diagnostics, statistical power and sample-size estimation, and the ability to synthesize findings into executive-level impact statements. It is commonly asked to determine whether a candidate can distinguish measurement or product regressions from marketing-mix shifts and to demonstrate both conceptual understanding of causal inference and practical application of data querying, metric definition, and experiment design in the Analytics & Experimentation domain.

  Analytics & ExperimentationHR Screen
• InstacartEasy·Data Scientist
  How to debug an apparent D14 retention drop

  This question evaluates competency in cohort-based retention analysis, understanding metric maturity and right-censoring, performing data quality and definition checks, and localizing real declines across segments, releases, or funnel changes.

  Analytics & ExperimentationTechnical Screen
• InstacartEasy·Data Scientist
  How would you investigate a metric decline?

  This question evaluates a data scientist's competency in diagnostic analytics, metric instrumentation awareness, causal reasoning, and segmentation-driven problem localization for consumer marketplace KPIs.

  Analytics & ExperimentationTechnical Screen
• InstacartMedium·Data Scientist
  Investigate Instacart Revenue Decline Using Weekly Data

  Instacart analytics prompt on investigating a 4% weekly revenue decline, covering time-series anomaly detection, seasonality, changepoints, prediction intervals, and richer-data decomposition by orders, AOV, geography, cohorts, and categories.

  Analytics & ExperimentationTechnical Screen
• InstacartEasy·Data Scientist
  Investigate marketplace metrics and experiment rollout

  This question evaluates competency in marketplace analytics, causal inference, experimentation design, metric instrumentation, cohort and retention analysis, and trade-off reasoning between core business metrics and secondary signals.

  Analytics & ExperimentationTechnical Screen
• InstacartMedium·Data Scientist
  Recommend and validate a budget allocation strategy

  This question evaluates a data scientist's competency in budget allocation, experimental design, KPI definition, segmentation, and causal inference by requiring translation of SQL-derived spend and profitability insights into constrained program- and advertiser-level budget rules while managing concentration risk.

  Analytics & ExperimentationTechnical Screen
• IntuitMedium·Data Scientist
  Build 30-day retention cohort table

  This problem tests your ability to aggregate user activity data to calculate 30-day retention rates, requiring joins, date arithmetic, and windowing functions.

  Analytics & ExperimentationTechnical ScreenSQLRetentionCohort Analysis
• IntuitMedium·Data Scientist
  Build cohort 30-day retention from signup date

  This problem tests your ability to manipulate time-series data to calculate cohort-based user retention rates while handling data censoring constraints.

  Analytics & ExperimentationTechnical ScreenSQLPandasRetention
• IntuitEasy·Data Scientist
  Design an experiment for pricing page redesign

  This question evaluates a candidate's competency in experiment design, causal inference, impact sizing, metric framework development, and statistical analysis in the context of a pricing page redesign that adds an annual subscription option.

  Analytics & ExperimentationTechnical Screen
• LinkedInHard·Data Scientist
  Analyze homepage drop and feed ranking

  This question evaluates a candidate's competency in product analytics, causal diagnosis, instrumentation and logging validation, segmentation and cohort analysis, and experimental design for feed ranking and user-path behavior.

  Analytics & ExperimentationTechnical Screen
• LinkedInHard·Data Scientist
  Design 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
• LinkedInMedium·Data Scientist
  Diagnose 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
• LinkedInEasy·Data Scientist
  Do US members upload more videos than non-US?

  This question evaluates a data scientist's competency in analytics and experimentation, specifically metric design, confounder control, cohorting, and statistical inference for comparing user behavior across segments.

  Analytics & ExperimentationTechnical Screen
• LinkedInMedium·Data Scientist
  Estimate 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
• LinkedInMedium·Data Scientist
  How to analyze Simpson's paradox

  This question evaluates competency in causal reasoning, statistical inference, experimental design, and detection of Simpson's paradox within the Analytics & Experimentation domain.

  Analytics & ExperimentationTechnical Screen
• LinkedInHard·Data Scientist
  How to diagnose traffic and measure relevance?

  This problem tests your ability to diagnose an unexplained drop in engagement metrics by decomposing a complex user funnel, isolating root causes using multidimensional data, and designing a robust validation framework.

  Analytics & ExperimentationTechnical ScreenRoot Cause AnalysisMetrics StrategyFunnel Analysis
• LinkedInMedium·Data Scientist
  Improve 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
• LinkedInHard·Data Scientist
  Measure Relevant Feed Success

  This question tests the ability to define North Star metrics for a social feed and design an experimentation framework that accounts for long-term ecosystem health and cross-side network effects.

  Analytics & ExperimentationTechnical ScreenProduct AnalyticsA/B TestingMetrics
• LinkedInMedium·Data Scientist
  Measure 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
• LinkedInMedium·Data Scientist
  Present 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
• LinkedInMedium·Data Scientist
  Resolve Conflicting A/B Test Results in Cities

  Evaluates interpretation of conflicting A/B test results across cities using Simpson's paradox and stratified estimands. Strong answers check weighting, SRM, heterogeneity, confidence intervals, and rollout criteria.

  Analytics & ExperimentationTechnical Screen
• LinkedInEasy·Data Scientist
  Resolve Simpson’s paradox in A/B email test

  This question tests your ability to identify and resolve Simpson's Paradox in A/B test results, where an aggregate trend disappears or reverses when segmented by user cohorts.

  Analytics & ExperimentationTechnical ScreenA/B TestingSimpson's ParadoxConfounding
• LinkedInMedium·Data Scientist
  Single 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