Data Scientist Interview Questions

Showing 601–630 of 1971 results

• DoorDashHard·Data Scientist
  Calculate Order Request Metrics

  This question evaluates SQL and data-manipulation competencies for computing order-level metrics, including aggregations, joins, windowed event sequencing, timestamp/timezone handling, and NULL-aware comparisons.

  Data Manipulation (SQL/Python)Onsite
• 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 rolling cold-delivery rates with windows

  This question evaluates proficiency with SQL window functions, time-based joins, event deduplication and business-rule implementation for computing rolling metrics over temporal windows.

  Data Manipulation (SQL/Python)Onsite
• 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
  Decide and test a 20% discount strategy

  This question tests the ability to design a controlled experiment to evaluate a promotional discount strategy, focusing on causality, profit optimization, and long-term customer value impact.

  Analytics & ExperimentationOnsiteCausal InferenceExperimentationUplift Modeling
• DoorDashHard·Data Scientist
  Decompose and optimize delivery operational costs

  This question evaluates a data scientist's ability to decompose per-order operational costs, identify high-impact levers to reduce unit economics, and design rigorous experiments and monitoring for a two-sided delivery marketplace, testing competencies in cost modeling, causal inference, experimentation design, and product analytics.

  Analytics & ExperimentationOnsite
• DoorDashHard·Data Scientist
  Define and compute retention and churn precisely

  This question evaluates a Data Scientist's competency in statistical measurement of user retention and churn, covering cohort definition, activity rules, risk-set-aware retention and churn formulas, censoring and delayed conversion handling, and survival-analysis concepts like time-to-churn, hazard, and cumulative incidence.

  Statistics & MathOnsite
• DoorDashHard·Data Scientist
  Define metrics for new market expansion success

  This question tests the ability to design a comprehensive evaluation framework for entering a new geographic market by balancing growth, unit economics, and marketplace balance.

  Analytics & ExperimentationOnsiteAnalyticsMarketplaceKPIs
• 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
• DoorDashMedium·Data Scientist
  Design Experiments to Measure Promotion Scheduling Impact

  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 Experiments to Measure Promotion Scheduling Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• DoorDashHard·Data Scientist
  Design a Homepage Store Recommender

  This question assesses the ability to design an end-to-end, low-latency recommendation system for a food delivery marketplace, focusing on candidate retrieval strategies, real-time personalization, and system architecture.

  Machine LearningOnsiteRecommender SystemsSystem DesignRanking
• DoorDashHard·Data Scientist
  Design a Low-Latency Store Recommender

  This question assesses the ability to architect a multi-stage recommendation pipeline that balances low-latency retrieval with high-accuracy ranking in a geo-aware, high-concurrency food delivery context.

  Machine LearningOnsiteSystem DesignRecommendation SystemsLatency
• DoorDashEasy·Data Scientist
  Design a Top Dasher experiment with interference

  This problem tests your ability to design a controlled experiment in a marketplace with network effects, specifically focusing on how to mitigate spillover interference between treated and control groups.

  Analytics & ExperimentationOnsiteExperimentationCausal InferenceMarketplace
• DoorDashHard·Data Scientist
  Design an experiment for order batching

  This question evaluates competence in experiment design and causal inference for marketplace interventions, covering choice of randomization unit and mitigation of interference/spillovers, stratification and covariate adjustment, power and sample‑size calculations, detection of novelty/steady‑state effects, heterogeneity estimation, and pre-registered guardrail decision rules. It is commonly asked in the Analytics & Experimentation domain because interviewers need to assess both conceptual understanding of causal inference and the practical application of randomized designs and operational measurement to ensure robust, contamination‑aware evaluation of policy changes.

  Analytics & ExperimentationOnsite
• 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
• DoorDashMedium·Data Scientist
  Design and Evaluate a Refund Policy for Delayed Orders

  Design an automated, data-driven compensation policy for late deliveries on a three-sided marketplace. Formulate delay thresholds, balance customer lifetime value against operational costs, and structure an A/B test with appropriate randomization and guardrail metrics.

  Analytics & ExperimentationOnsiteexperimentationab-testingmarketplace-analytics
• 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
• DoorDashHard·Data Scientist
  Design and analyze batching algorithm experiment

  This problem tests the ability to design a cluster-randomized experiment in a marketplace setting, focusing on managing network effects (spillover) and defining appropriate inference strategies for geo-based batches.

  Analytics & ExperimentationOnsiteExperimentationCausal InferenceMarketplace
• DoorDashHard·Data Scientist
  Design and evaluate an uplift model

  This question evaluates competency in causal inference and uplift modeling, experimental design for randomized treatment allocation, offline evaluation of uplift models, operational decision rules under budget constraints, fairness guardrails, and handling interference and deployment of sequential tests in a data-science context.

  Machine LearningOnsite
• DoorDashHard·Data Scientist
  Design experiments for marketplace product changes

  This question evaluates experiment design, causal inference, product-analytics, and metric-definition skills for multi-sided marketplaces, focusing on handling interference, spillovers, heterogeneity, and trade-offs between conversion, retention, credit burn, and monetization.

  Analytics & ExperimentationOnsite
• DoorDashEasy·Data Scientist
  Design experiments for payments, search, and promotions

  This question tests your ability to design robust A/B tests for marketplace products, focusing on metric selection, identifying interference, and accounting for ecosystem-level trade-offs.

  Analytics & ExperimentationOnsiteA/B TestingMarketplace AnalyticsCausal Inference
• 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 with Key Metrics Analysis

  DoorDash analytics prompt on diagnosing cold food deliveries, covering complaint metrics, lifecycle timestamps, batching, packaging, weather, segmentation, data quality, A/B testing, and operational guardrails.

  Analytics & ExperimentationOnsite
• 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 Why Delivered Food Arrives Cold

  Investigate an increase in cold food complaints by decomposing fulfillment timestamps, evaluating mix shifts versus operational degradation, and designing a mitigation experiment with balanced marketplace guardrails.

  Analytics & ExperimentationOnsiteanalyticsexperimentationfunnel-analysis
• DoorDashMedium·Data Scientist
  Diagnose a Decline in Order Acceptance Rate

  Evaluate a sudden drop in Dasher order acceptance rate by verifying data integrity, decomposing segment-level shifts, and designing an experiment to mitigate downstream marketplace impacts.

  Analytics & ExperimentationOnsiteProduct AnalyticsMetric DecompositionA/B Testing
• 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