Data Scientist Interview Questions

Showing 331–360 of 1076 results

• DiscordMedium·Data Scientist
  Design Product Notification and Autocomplete Experiments

  This question evaluates experimental design, causal inference, metric definition, selection bias analysis, logging and privacy considerations, and product analytics competencies for a data scientist working in Analytics & Experimentation.

  Analytics & ExperimentationTechnical Screen
• DiscordMedium·Data Scientist
  Evaluate Model-Targeted Subscription Notifications

  Evaluate subscription-targeting notifications with incrementality metrics, A/A preparation, flat-result diagnosis, and alternative causal designs.

  Analytics & ExperimentationTechnical Screen
• DiscordMedium·Data Scientist
  Explain a Business Project That Used Causal Inference

  Prepare a causal-inference project answer covering the business decision, identification assumptions, uncertainty, and lessons for a repeat study.

  Behavioral & LeadershipTechnical Screen
• DiscordMedium·Data Scientist
  Return Words Present in Exactly One Message

  Find words appearing in exactly one of two messages using set symmetric difference while clarifying tokenization and case rules.

  Coding & AlgorithmsTechnical Screen
• DoorDashMedium·Data Scientist
  Analyze Driver Requests for Food Delivery Orders

  This question evaluates data manipulation and analytical SQL skills in the Data Manipulation (SQL/Python) domain, focusing on window functions for ranking and temporal comparison, joins, and aggregation when analyzing orders and driver requests.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashHard·Data Scientist
  Assess Success Criteria for Bike-Courier Delivery Launch

  Evaluates marketplace analytics for launching a bike-courier delivery option in dense delivery markets. Strong answers define stakeholder value, success metrics, market selection, phased rollout, guardrails, and causal evaluation under network effects.

  Analytics & ExperimentationTechnical Screen
• DoorDashMedium·Data Scientist
  Calculate Late Delivery Percentage and Top Customers

  This question evaluates data manipulation and aggregation skills for a Data Scientist role, focusing on date handling, computing the percentage of late orders, and ranking customers using SQL or Python.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashEasy·Data Scientist
  Calculate Monthly Restaurant Sales Growth

  This problem tests the ability to aggregate transactional data by month and use window functions to calculate percentage growth between consecutive periods while handling null values.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsAggregation
• DoorDashMedium·Data Scientist
  Calculate power and test duration

  This question evaluates proficiency in statistical power analysis, sample size calculation for proportions, hypothesis testing for two-sided z-tests, and practical experiment duration estimation for A/B testing.

  Statistics & MathTechnical Screen
• DoorDashMedium·Data Scientist
  Compute sample sizes and error control

  This question evaluates proficiency in experimental design and applied inferential statistics—specifically sample size calculation for means and proportions, non-inferiority testing, multiple-comparison error control, cluster-randomized design effects, and sequential monitoring boundaries—within the Statistics & Math domain for a Data Scientist role. It is commonly asked to measure the ability to apply statistical formulas and error-control principles under practical constraints, balancing power and type I error while accounting for clustering and interim looks; the assessment emphasizes practical application grounded in conceptual understanding of inferential methods.

  Statistics & MathTechnical Screen
• DoorDashHard·Data Scientist
  Design A/B Test to Evaluate Algorithm's Revenue Impact

  DoorDash statistics and experimentation prompt on A/B testing a recommendation algorithm's revenue impact, covering hypotheses, randomization, sample size, heavy-tailed revenue, p=0.08 interpretation, and causal alternatives.

  Statistics & MathTechnical Screen
• DoorDashMedium·Data Scientist
  Design Biker metrics and A/B test plan

  This question tests the ability to design an experimentation framework for a two-sided marketplace, focusing on metric definition, causal inference, and identifying potential gaming behaviors in delivery logistics.

  Analytics & ExperimentationTechnical ScreenA/B TestingMarketplace AnalyticsMetric Design
• DoorDashHard·Data Scientist
  Design analysis to reduce cold-delivery complaints

  This problem tests the ability to diagnose a service quality issue, define key performance indicators, and design an experiment to evaluate an operational intervention while controlling for confounding variables.

  Analytics & ExperimentationTechnical ScreenCausal InferenceExperimental DesignProduct Metrics
• DoorDashHard·Data Scientist
  Design and analyze a switchback experiment

  This question evaluates proficiency in experimental design, causal inference, randomization and contamination control, regression modeling with fixed effects and clustered standard errors, power calculation, and handling of compliance and robustness issues.

  Analytics & ExperimentationTechnical Screen
• DoorDashMedium·Data Scientist
  Determine Success Metrics for Biker Dasher Program Launch

  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 Determine Success Metrics for Biker Dasher Program Launch states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• DoorDashMedium·Data Scientist
  Diagnose Cold-Food Deliveries and Make a Launch Decision

  Work through a product analytics case about diagnosing a delivery-quality problem and deciding whether an intervention should launch. The question tests metric hierarchy, causal reasoning, investigation design, power considerations, and decision criteria.

  Analytics & ExperimentationTechnical Screen
• DoorDashMedium·Data Scientist
  Diagnose Decline in Successful Orders

  This question evaluates a data scientist's skills in marketplace analytics, causal reasoning, metric framework design, and experimentation for diagnosing a decline in successful orders.

  Analytics & ExperimentationTechnical Screen
• DoorDashHard·Data Scientist
  Diagnose LA completed-order drop and design experiment

  This question evaluates a data scientist's competency in funnel-based diagnostics, causal inference, and experimental design for distinguishing supply-side versus demand-side drivers in a two-sided marketplace, emphasizing metric selection, segmentation, and guardrail monitoring.

  Analytics & ExperimentationTechnical Screen
• DoorDashMedium·Data Scientist
  Diagnose a One-Day Drop in Successful Deliveries

  Tests the candidate's structured metric troubleshooting, funnel decomposition, and root cause analysis for an acute drop in core marketplace volume. Evaluates the ability to separate count effects from conversion rate changes and isolate partner-specific vs. market-wide anomalies.

  Analytics & ExperimentationTechnical Screenmetric-troubleshootingfunnel-analysismarketplace-analytics
• DoorDashHard·Data Scientist
  Diagnose and experiment to reduce late deliveries

  This question evaluates a data scientist's competencies in causal inference, metrics engineering, data instrumentation and joining, root‑cause analysis, and experiment design focused on operational delivery performance.

  Analytics & ExperimentationTechnical Screen
• DoorDashHard·Data Scientist
  Diagnose and reduce cold-food refund costs

  This problem tests the ability to diagnose root causes of operational cost drivers and design a rigorous experiment to validate potential mitigation policies.

  Analytics & ExperimentationTechnical ScreenCausal InferenceExperimentationMarketplace Analytics
• 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
• DoorDashEasy·Data Scientist
  Find Monthly Top Customers Excluding High-Frequency Users

  This problem tests proficiency in window functions, filtering based on aggregated metrics, and handling ranking logic in SQL.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsData Aggregation
• DoorDashMedium·Data Scientist
  Generate Weekly Revenue and Engagement Summary with Pandas

  This question evaluates proficiency in data manipulation and analytics with Pandas and SQL, covering aggregations, distinct purchase counts, lambda-based user-tier classification, and platform-level conversion-rate computation.

  Data Manipulation (SQL/Python)Technical Screen
• 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
• DoorDashMedium·Data Scientist
  Identify Challenges and Solutions for Bike-Delivery Program

  Evaluates operational and strategic thinking for a bike-delivery courier program. Strong answers identify marketplace, safety, weather, regulatory, courier, merchant, customer-experience, and unit-economics risks, then pair each challenge with practical mitigations and measurable rollout guardrails.

  Behavioral & LeadershipTechnical Screen
• 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