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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
- Companies
- 11
- Topics
- 8
- Languages
Showing 271–300 of 641 results
- J.P. MorganMediumData ScientistExplain C++ virtual dispatch and object lifetime
This question tests the candidate's understanding of C++ memory management, the mechanics of the virtual method table (vtable), and the importance of virtual destructors in polymorphic inheritance.
Software Engineering FundamentalsOnsiteC++Memory ManagementPolymorphism - J.P. MorganMediumData ScientistSolve tiling, probability, and nested-radical puzzles
This problem tests your ability to derive recurrence relations for combinatorial tiling, solve infinite series limits, and calculate exact probabilities using combinations.
Coding & AlgorithmsOnsiteCombinatoricsDynamic ProgrammingProbability - J.P. MorganMediumData ScientistTest whether samples follow a binomial distribution
This problem tests the application of goodness-of-fit testing to verify if a set of discrete observations conforms to a binomial distribution.
Machine LearningOnsiteStatisticsHypothesis TestingProbability - LinkedInHardData ScientistDesign Experiments for Email Campaign & Messaging Update
Evaluates experiment design for concurrent email and in-product messaging launches with possible interaction effects. Strong answers use factorial designs, power calculations, interaction terms, sequential testing, and guardrails.
Analytics & ExperimentationOnsite - LinkedInMediumData ScientistDiagnose a Drop in Applications from Job Recommendation Emails
Diagnose falling applications from job emails by tracing funnel rates, recommendation relevance, promoted-job mix, and ranking changes.
Analytics & ExperimentationOnsite - LinkedInMediumData ScientistEstimate Redesign Impact Using Propensity Score Matching
Estimate Redesign Impact Using Propensity Score Matching 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 - LinkedInMediumData ScientistEstimate an Experiment’s Mean Effect from Cohort and Metric Tables
Estimate experimental impact from cohort and timestamped metric data using user-level outcomes, difference in means, confidence intervals, and valid testing.
Statistics & MathOnsite - LinkedInMediumData ScientistImprove Profile Completion Rate
Evaluates analytics and experimentation strategy for improving LinkedIn profile completion. Strong answers define value-weighted completion metrics, segment gaps, prioritize interventions, and test impact with guardrails.
Analytics & ExperimentationOnsite - LinkedInHardData ScientistMeasure Causal Impact of Self-Selected App Redesign
LinkedIn causal inference prompt on measuring a self-selected app redesign, covering potential outcomes, ATT, propensity matching or weighting, staggered difference-in-differences, event studies, covariates, balance, and pre-trend validation.
Statistics & MathOnsite - LinkedInMediumData ScientistMeasure Success of New B2B Product
Evaluates success measurement for a new LinkedIn B2B SaaS product at account and seat levels. Strong answers define activation, engagement, value, retention, expansion, anomaly analysis, and growth prioritization.
Analytics & ExperimentationOnsite - LinkedInMediumData ScientistPresent Insights from Course Supply and Participation Data
Analyze course catalog supply and learner participation using time trends, category comparisons, utilization measures, and evidence-based presentation.
Analytics & ExperimentationOnsite - LinkedInMediumData ScientistSingle Queue vs Multiple Queues — Service Design
Evaluates queueing-system reasoning for single-line versus multiple-line bank teller designs. Strong answers compare M/M/c pooling with parallel M/M/1 queues, waiting-time averages, variance, stability, fairness, and operations.
Analytics & ExperimentationOnsite - LinkedInMediumData ScientistTest a New Feature at Its Natural Product Placement
Design a feature launch experiment that separates natural-placement availability, discoverability, promotional exposure, and downstream product value.
Analytics & ExperimentationOnsite - LyftMediumData ScientistAssess Cultural Fit Through Behavioral Interview Questions
Assess Cultural Fit Through Behavioral Interview Questions evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - LyftMediumData ScientistCompute Poisson supply–demand match probability
This question evaluates understanding of probability distributions and stochastic modeling—specifically Poisson processes, the Skellam distribution for differences, expectation of minima, and probabilistic capacity planning within the Statistics & Math domain.
Statistics & MathOnsite - LyftMediumData ScientistDemonstrate leadership under ambiguity
This question evaluates leadership, stakeholder communication, ambiguity management, and negotiation competencies for a Data Scientist role, covering problem scoping, assumption documentation, rapid validation, cross-cultural interactions, and data-backed compensation discussions.
Behavioral & LeadershipOnsite - LyftMediumData ScientistDesign experiments for marketplace balance
This question evaluates a data scientist's competency in experimental design, causal inference, metric definition, statistical power calculation, and operational rollout planning for two-sided marketplaces, and it is commonly asked to assess the ability to account for interference, supply constraints, spillovers, and contamination when measuring treatment effects. Categorized under Analytics & Experimentation, it tests both conceptual understanding and practical application by probing exposure and eligibility definitions, metric and guardrail specification, sample-size and clustering adjustments, recognition of heterogeneous treatment effects and sequential monitoring concerns, variance-reduction considerations, and rollout and fallback decision-making.
Analytics & ExperimentationOnsite - LyftMediumData ScientistInvestigate Causes of Driver WOW Score Drop
Investigate Causes of Driver WOW Score Drop 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 - LyftMediumData ScientistOptimize Driver Repositioning for Minimal Pickup Time
This question evaluates spatial algorithms and real-time optimization for ride dispatch together with interval scheduling and capacity planning competencies, covering skills in computational geometry, routing, demand forecasting, and temporal overlap reasoning.
Coding & AlgorithmsOnsite - LyftMediumData ScientistQuery and transform marketplace data in SQL/Python
This question evaluates mastery of data manipulation and analytics skills in the Data Manipulation (SQL/Python) domain, including SQL aggregation and window functions, time-based joins and grouping, deduplication of status streams, handling late-arriving events, and implementing rolling metrics using both SQL and pandas.
Data Manipulation (SQL/Python)Onsite - MetaHardData ScientistAnalyze Algorithm's Impact on Diverse Demographics and Validate Causes
Analyze Algorithm's Impact on Diverse Demographics and Validate Causes 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 - MetaHardData ScientistAnalyze Change in App Metrics and Feature 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 Analyze Change in App Metrics and Feature Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistAnalyze Comment Distribution Using Statistical Metrics and Tests
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 Analyze Comment Distribution Using Statistical Metrics and Tests states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistAnalyze Hashtag Follow Behavior with SQL Queries
This question evaluates a candidate's competency in data manipulation and analytical SQL techniques—specifically aggregation, filtering, joins, and calculation of proportions—to analyze hashtag follow behavior.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistAnalyze Key Metrics for Notification System Success
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 Analyze Key Metrics for Notification System Success states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistAnalyze Oculus App Engagement with SQL Queries
This question evaluates proficiency in SQL-based data manipulation and aggregation, time-window filtering, percentage calculations, and the application of basic statistical hypothesis testing for comparing user engagement across categories.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistAnalyze Revenue Shifts to Identify Cannibalization Effects
Evaluates revenue cannibalization analysis across creation sources. Strong answers define Source A, other-source revenue, total revenue, cannibalization ratio, and net lift, then use panel, rollout, difference-in-differences, or synthetic-control evidence to separate source shifting from organic growth.
Analytics & ExperimentationOnsite - MetaMediumData ScientistAnalyze Thirty-Day Ad Performance with SQL
This problem tests proficiency in window functions, time-based aggregation, and join logic to analyze multi-dimensional advertising performance metrics.
Data Manipulation (SQL/Python)OnsiteSQLAggregationTime-Series - MetaMediumData ScientistAnalyze User Comment Distribution and Sampling Effects
Evaluates statistical reasoning about right-skewed user comment counts and sampling distributions. Strong answers place median, mean, and p95 correctly, then use the Central Limit Theorem to explain why group averages become less variable and more symmetric.
Statistics & MathOnsite - MetaMediumData ScientistAnalyze User Transfer Distribution in Initial Launch Period
Evaluates distribution analysis for transfer counts after a new P2P payments launch. Strong answers specify the denominator, describe zero inflation and right skew, locate mean, median, mode, and p95, and explain how adoption, cohorts, and exposure change the distribution over time.
Statistics & MathOnsite
J.P. Morgan
LinkedIn
Lyft
Meta