Data Scientist Interview Questions

Showing 151–180 of 513 results

• DoorDashHard·Data Scientist
  Diagnose cold-food spike and design experiments

  This question evaluates a data scientist's skills in product analytics, metric design, diagnostics, causal inference, and experimental design for diagnosing cold-food complaint spikes using logs, GPS, weather and complaint labels.

  Analytics & ExperimentationOnsite
• DoorDashMedium·Data Scientist
  Diagnose completed orders drop in Los Angeles

  This question evaluates a data scientist's competency in product analytics, causal inference, funnel and metric decomposition, segmentation, and experimentation for marketplace platforms, focusing on diagnosing sustained drops in completed orders.

  Analytics & ExperimentationOnsite
• DoorDashHard·Data Scientist
  Evaluate Account-Partner Onboarding with Success 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 Evaluate Account-Partner Onboarding with Success Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• DoorDashHard·Data Scientist
  Evaluate Biker Feature Success

  This question evaluates experimentation and product analytics skills—metric design, causal inference, and marketplace impact assessment—in the context of a two-sided delivery marketplace, with attention to eligibility, routing, and matching effects.

  Analytics & ExperimentationOnsite
• DoorDashMedium·Data Scientist
  Evaluate Courier-Selected Delivery Distance Limits

  This question tests your ability to design an A/B test for a marketplace feature while accounting for network effects, trade-offs between supply-side control and demand-side fulfillment, and selecting appropriate metrics.

  Analytics & ExperimentationOnsiteA/B TestingMarketplace AnalyticsCausal Inference
• DoorDashHard·Data Scientist
  Evaluate Dasher Initiatives with A/B Testing and 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 Evaluate Dasher Initiatives with A/B Testing and Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• DoorDashMedium·Data Scientist
  Evaluate Impact of $1 Fee on Fast-Food Profitability

  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 Evaluate Impact of $1 Fee on Fast-Food Profitability states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• DoorDashHard·Data Scientist
  Evaluate Top-Dasher Program's Benefits and Challenges

  Evaluate Top-Dasher Program's Benefits and Challenges 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
• DoorDashMedium·Data Scientist
  Evaluate a Bike Dasher Program with a Controlled Experiment

  Tests marketplace experimentation design, tackling spatial-temporal spillover effects, metric selection, and decision criteria for a multimodal courier fleet.

  Analytics & ExperimentationTechnical ScreenA/B TestingMarketplace ExperimentationCausal Inference
• DoorDashMedium·Data Scientist
  Evaluate a Bike-Delivery Pilot with Marketplace Metrics

  This problem tests the ability to design an A/B test for a logistics marketplace, focusing on metric definition, experiment randomization strategy, and identifying potential confounding variables.

  Analytics & ExperimentationTechnical ScreenA/B TestingMarketplaceLogistics
• DoorDashHard·Data Scientist
  Evaluate a new ranking model

  This question evaluates expertise in experimentation design and causal inference within two-sided marketplace environments. It tests the ability to handle interference, SUTVA violations, metric selection, and rollout safety when deploying ranking model upgrades — core competencies for data scientist roles focused on product analytics and A/B testing.

  Analytics & ExperimentationOnsite
• DoorDashHard·Data Scientist
  Experiment on increasing order notifications

  This question evaluates a data scientist's skills in experimental design, causal inference, metric definition and aggregation, power/MDE calculation, and cross‑channel attribution for order‑related messaging campaigns.

  Analytics & ExperimentationOnsite
• DoorDashEasy·Data Scientist
  How would you test a bike delivery option?

  This problem tests the ability to design an A/B test, define success metrics, and account for confounding variables when introducing a new logistics channel (bike delivery) to a marketplace.

  Analytics & ExperimentationTechnical ScreenA/B TestingMetricsMarketplace Analytics
• DoorDashHard·Data Scientist
  Identify Key Drivers of Delivery Decline in Los Angeles

  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 Key Drivers of Delivery Decline in Los Angeles states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• DoorDashMedium·Data Scientist
  Investigate Causes of Cold Food Deliveries and Solutions

  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 Investigate Causes of Cold Food Deliveries and Solutions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• DoorDashMedium·Data Scientist
  Investigate Causes of Cold Meal Deliveries

  DoorDash data scientist case on cold meal deliveries, covering lifecycle timestamps, complaint metrics, segmentation, root-cause analysis, dispatch and packaging fixes, A/B testing, and marketplace guardrails.

  Analytics & ExperimentationTechnical Screen
• DoorDashMedium·Data Scientist
  Investigate Causes of Increased Driver Wait Time

  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 Investigate Causes of Increased Driver Wait Time states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• DoorDashHard·Data Scientist
  Investigate Declining Successful Orders

  This question tests your ability to decompose a high-level drop in a key business metric into granular segments and formulate testable hypotheses to explain the underlying root causes.

  Analytics & ExperimentationTechnical ScreenProduct AnalyticsFunnel AnalysisHypothesis Testing
• DoorDashHard·Data Scientist
  Investigate Falling Successful Orders

  This question evaluates a data scientist's ability to diagnose a drop in a product metric through anomaly verification, data validity checks, funnel and cohort analysis, causal attribution across marketplace sides (customer, restaurant, delivery), and experiment design.

  Analytics & ExperimentationTechnical Screen
• DoorDashHard·Data Scientist
  Investigate Falling Successful Orders in LA

  This problem tests your ability to decompose a drop in a high-level business metric into granular segments, formulate testable hypotheses, and propose methodologies to identify the root cause.

  Analytics & ExperimentationTechnical ScreenRoot Cause AnalysisFunnel AnalyticsMarketplace Dynamics
• DoorDashMedium·Data Scientist
  Investigate LA Order Drop

  This question evaluates marketplace analytics, causal inference, hypothesis-driven investigation, funnel decomposition, and experimentation design skills in a data science context.

  Analytics & ExperimentationTechnical Screen
• DoorDashMedium·Data Scientist
  Investigate Pop-up Impact on Partner Referral Conversions

  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 Investigate Pop-up Impact on Partner Referral Conversions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• DoorDashMedium·Data Scientist
  Measure Impact of Merchant Variety on Consumer Experience

  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 Measure Impact of Merchant Variety on Consumer Experience states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• DoorDashEasy·Data Scientist
  Reduce Cold-Food Incidents with Metrics and an Insulated-Bag Experiment

  Design an A/B test and metric framework to evaluate whether equipping couriers with insulated bags reduces cold-food complaints while guarding marketplace efficiency.

  Analytics & ExperimentationTechnical ScreenExperimentationA/B TestingMetrics Design
• DoorDashMedium·Data Scientist
  Should DoorDash add bicycle dashers?

  This problem tests the ability to design an A/B test for a marketplace intervention, identify success metrics, and account for potential cannibalization and external factors in urban delivery.

  Analytics & ExperimentationTechnical ScreenA/B TestingMarketplace AnalyticsCausal Inference
• DropboxMedium·Data Scientist
  Run org-safe online experiment for recommender

  This question evaluates a data scientist's competency in online experimentation design, causal inference, clustered randomization, metric definition (primary, secondary, guardrails), sample size/MDE computation, variance reduction and sequential monitoring, instrumentation and privacy-aware logging.

  Analytics & ExperimentationTechnical Screen
• ExpediaHard·Data Scientist
  Choose KPIs and prove impact with experiments

  This question evaluates a data scientist's competency in KPI engineering, causal inference, experiment design, metric validation, and multi-touch attribution within the Analytics & Experimentation domain and is focused on profit-oriented search ranking.

  Analytics & ExperimentationOnsite
• Fetch RewardsMedium·Data Scientist
  Design analysis to validate reported decline

  This question evaluates analytical and experimental-design competencies including metric specification, time-series and same-store comparisons, bias and confounder detection, and concise data-driven communication under a tight time constraint.

  Analytics & ExperimentationTechnical Screen
• Fetch RewardsMedium·Data Scientist
  Evaluate Overlapping Shelf Ranking Experiments

  This question evaluates competency in experimental design and causal inference for A/B testing, focusing on handling overlapping traffic, interaction effects between concurrent experiments, metric selection, guardrails, and statistical validity.

  Analytics & ExperimentationTechnical Screen
• FigmaMedium·Data Scientist
  Clarify an Ambiguous Product Question and Define a Metric

  This question tests the ability to transform an abstract product objective into a rigorous measurement framework, focusing on metric selection and experimental design.

  Analytics & ExperimentationOnsiteAnalyticsProduct MetricsExperimentation