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Data Scientist Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
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- 11
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- 8
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Showing 181–210 of 641 results
- DoorDashMediumData ScientistDesign 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 - DoorDashHardData ScientistDesign 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 - DoorDashHardData ScientistDesign 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 - DoorDashEasyData ScientistDesign 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 - DoorDashHardData ScientistDesign 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 - DoorDashMediumData ScientistDesign 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 - DoorDashHardData ScientistDesign 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 - DoorDashHardData ScientistDesign 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 - DoorDashHardData ScientistDesign 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 - DoorDashEasyData ScientistDesign 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 - DoorDashMediumData ScientistDiagnose 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 - DoorDashMediumData ScientistDiagnose 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 - DoorDashMediumData ScientistDiagnose 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 - DoorDashHardData ScientistDiagnose 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 - DoorDashMediumData ScientistDiagnose 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 - DoorDashHardData ScientistEvaluate 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 - DoorDashHardData ScientistEvaluate 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 - DoorDashMediumData ScientistEvaluate 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 - DoorDashHardData ScientistEvaluate 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 - DoorDashMediumData ScientistEvaluate 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 - DoorDashHardData ScientistEvaluate 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 - DoorDashHardData ScientistEvaluate 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 - DoorDashHardData ScientistExperiment 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 - DoorDashHardData ScientistExplain interest and influence stakeholders
This question evaluates stakeholder management, cross-functional influence, data-driven decision-making, ownership, and the ability to reason about multi-sided marketplace dynamics for a Data Scientist role.
Behavioral & LeadershipOnsite - DoorDashMediumData ScientistHow would you mentor junior teammates?
A DoorDash Data Scientist onsite behavioral & leadership question that asks how you mentor others as a senior IC — especially more junior teammates. Strong answers diagnose each person's gaps, tailor support across levels and cross-functional partners, use concrete mechanisms (ramp plans, feedback rubrics, scalable templates, sponsorship), mentor through influence rather than authority, and measure effectiveness via independence and impact, all anchored in a detailed STAR example.
Behavioral & LeadershipOnsite - DoorDashMediumData ScientistInvestigate 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 - DoorDashMediumData ScientistMeasure 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 - DoorDashMediumData ScientistModel schema and query new-market readiness
This question evaluates SQL-based data manipulation skills including time-series aggregation, cohort analysis, percentile/median calculations, joins between event and reference tables, and deriving operational KPIs for new-market readiness; it targets the Data Manipulation (SQL/Python) domain for a Data Scientist role.
Data Manipulation (SQL/Python)Onsite - DoorDashHardData ScientistPrioritize projects and manage tight deadlines
This question evaluates prioritization, scope definition, stakeholder leadership, resource and risk management competencies for a Data Scientist in the Behavioral & Leadership category.
Behavioral & LeadershipOnsite - DoorDashMediumData ScientistResolve Conflicts and Deliver Results Under Pressure
DoorDash data scientist behavioral prompt on cross-functional disagreement, delivering under limited data or resources, receiving critical feedback, STAR-L storytelling, measurable results, and reflection.
Behavioral & LeadershipOnsite
DoorDash