Data Scientist Interview Questions

Showing 1–14 of 14 results

• AirbnbMedium·Data Scientist
  Walk Through an Experiment From Design to Decision

  Walk through an end-to-end A/B test for an online marketplace feature, covering metric definition, randomization design, SRM diagnosis, and making a launch decision with conflicting trade-offs.

  Analytics & ExperimentationHR ScreenExperimentationA/B TestingProduct Analytics
• AmazonHard·Data Scientist
  Design causal study for reminder impact

  This question evaluates a data scientist's competency in observational causal inference and program evaluation, covering concepts such as staggered rollouts, treatment definition, difference-in-differences diagnostics, matching/weighting, negative controls, interference handling, and power/MDE planning.

  Analytics & ExperimentationHR Screen
• Capital OneEasy·Data 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 OneMedium·Data 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 OneMedium·Data 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
• DisneyHard·Data Scientist
  Diagnose and decide on watch-time drop

  This question evaluates a data scientist's skills in causal inference, experiment analytics, metric definition and guardrails, confounder adjustment, and rapid decision-making under time pressure.

  Analytics & ExperimentationHR Screen
• GoogleEasy·Data Scientist
  Design an A/B test for search ranking

  This question tests the ability to design a statistically sound A/B test for a search ranking algorithm, focusing on metric selection, hypothesis formulation, and common pitfalls in experimentation.

  Analytics & ExperimentationHR ScreenA/B TestingMetricsHypothesis Testing
• 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
• MetaHard·Data Scientist
  Design a clustered A/B test with spillovers

  This question tests the ability to design a cluster-randomized experiment when interactions between users (network interference) violate the stable unit treatment value assumption (SUTVA).

  Analytics & ExperimentationHR ScreenCausal InferenceExperimental DesignStatistics
• NetflixMedium·Data Scientist
  Design experiment on culture memo emphasis

  This question tests the ability to design a rigorous A/B test for a qualitative product change, focusing on metric selection, potential bias mitigation, and pre-experimental planning.

  Analytics & ExperimentationHR ScreenA/B TestingExperiment DesignMetrics
• SamsungMedium·Data Scientist
  Plan parking demand for a retail store

  This question evaluates demand forecasting, capacity planning, causal inference, and experimental-design competencies within data science, focusing on translating business objectives into measurable metrics such as peak occupancy, overflow rate, lost visits, and return on investment.

  Analytics & ExperimentationHR Screen
• TikTokMedium·Data Scientist
  Analyze promo anomaly and design risk guardrails

  This question evaluates a candidate's ability to diagnose promotional anomalies, distinguish coordinated abuse from organic virality, design real-time approve/hold/reject decisioning with cost trade-offs, and specify experiment and monitoring plans, testing competencies in fraud analytics, operational risk modeling, and experimentation.

  Analytics & ExperimentationHR Screen
• TikTokHard·Data Scientist
  Design an interference-robust A/B test for monetization

  This question evaluates experimental design and causal inference skills within Analytics & Experimentation, emphasizing interference mitigation, clustering and randomization choices, eligibility and exposure rules, metric hierarchy and guardrails, power and duration planning, variance-reduction techniques, and cluster-robust inference in a two-sided marketplace. It is commonly asked to test a data scientist's practical-application ability to balance monetization and growth trade-offs through precise statistical planning, pre-registered stopping rules, SRM detection, MDE and rollout decision thresholds rather than purely conceptual understanding.

  Analytics & ExperimentationHR Screen
• UpstartHard·Data Scientist
  Formulate hypotheses and metrics for video-pin ramp

  This question evaluates experiment-design and data-science competencies including hypothesis specification, metric choice and guardrails, segmentation, interference handling, rollout planning, power/MDE considerations, and data-quality monitoring, and it falls under the Analytics & Experimentation domain.

  Analytics & ExperimentationHR Screen