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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 391–420 of 1971 results
- Capital OneMediumData ScientistDesign a better water bottle and test it
This question evaluates experimental design, product analytics, hypothesis formulation, metric selection, sample size/power/MDE estimation, segmentation, and risk and rollback planning within the Analytics & Experimentation domain for Data Scientist roles.
Analytics & ExperimentationOnsite - Capital OneHardData ScientistDesign a production face recognition system
This question evaluates production ML system design and engineering skills specific to face recognition, covering on-device model selection and embedding design, training objectives and evaluation protocols, anti-spoofing and robustness mechanisms, privacy and template protection, performance/resource constraints, fairness and monitoring, and safe experimentation. It is asked in the Machine Learning domain for Data Scientist roles because it assesses trade-offs between privacy, security, latency, and scalability in real-world systems, and it tests both conceptual understanding of biometric and security principles and practical application for deploying and operating on-device ML.
Machine LearningOnsite - Capital OneHardData ScientistDesign a profit growth strategy
This question evaluates a data scientist's competency in product analytics, marketplace economics, pricing strategy, demand elasticity estimation, experimentation, and quantitative impact sizing.
Analytics & ExperimentationTechnical Screen - Capital OneHardData ScientistDesign and analyze a card signup A/B test
This question evaluates a data scientist's competency in A/B test design, causal inference, statistical power and sample-size calculation, risk-adjusted profit metrics, sequential testing, identity resolution, and monitoring for credit and fraud impacts.
Analytics & ExperimentationTechnical Screen - Capital OneHardData ScientistDesign and analyze ad A/B test
This question evaluates a candidate's competency in online experimentation and statistical analysis, covering hypothesis formulation, variance reduction, clustering and stratification, multiplicity control, power/MDE calculation, and operational metric guardrails.
Analytics & ExperimentationOnline Assessment - Capital OneHardData ScientistDesign and analyze ads A/B test this week
This question evaluates experimental design and applied statistical analysis skills for A/B testing, covering randomization, exposure control, stratification, variance reduction methods, metric formalization, hypothesis testing, sample sizing, sequential monitoring, diagnostics, and handling delayed offline conversions.
Analytics & ExperimentationOnline Assessment - Capital OneHardData ScientistDesign and analyze an SBA mini case experiment
This question evaluates expertise in experimental design and causal inference, including sample-size and power calculations, ITT/TOT analysis, variance reduction techniques, heterogeneity measurement, data quality and fraud handling, guardrail monitoring, and rollout decision-making for product experiments.
Analytics & ExperimentationTechnical Screen - Capital OneMediumData ScientistDesign cloud product packaging and pricing strategy
This question evaluates a candidate's product strategy and quantitative business competencies, including pricing and packaging design, unit economics, risk assessment, and the ability to communicate trade-offs to executives.
Behavioral & LeadershipTechnical Screen - Capital OneMediumData ScientistDesign fundraising experiment and guardrails
This question evaluates experimental design, statistical power and sample-size calculation, selection of primary and guardrail metrics, segmentation with multiple-testing control or hierarchical modeling, stopping rules for sequential analysis, and revenue-impact calculations for A/B testing of email solicitations.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistDesign metrics and an A/B test for an app
This question evaluates product analytics and experimentation competencies—product framing, unit economics, revenue modeling, computation-ready metric design, statistical power/sample-size calculation, A/B test setup, and cost-benefit/breakeven analysis—for a Data Scientist role in the Analytics & Experimentation domain, and is commonly asked to assess the ability to translate product hypotheses into measurable business outcomes and defend decisions under stakeholder scrutiny. It tests both conceptual understanding of segmentation, metric validity, and bias considerations and practical application in defining precise numerators/denominators, computing minimum sample sizes and test duration, and producing actionable ROI models.
Analytics & ExperimentationTechnical Screen - Capital OneHardData ScientistDesign profit evaluation for loyalty program
This question evaluates a data scientist's competency in causal inference, experimental and quasi-experimental design, incremental profit measurement from transaction-level data, statistical power and sample-size estimation, and validation and guardrail metric planning.
Analytics & ExperimentationTechnical Screen - Capital OneEasyData ScientistDesign robber detection from surveillance video
This question assesses the ability to design an end-to-end computer vision pipeline for detecting rare, high-stakes events in real-time video surveillance.
Machine LearningTechnical ScreenComputer VisionSystem DesignAnomaly Detection - Capital OneMediumData ScientistDetermine Claim Rate for Breakeven in Insurance Portfolio
Evaluates weather-insurance portfolio profitability from premiums, servicing, regulatory costs, and expected claims. Strong answers derive break-even claim rate, segment-selection rules, and incremental profit formulas.
Statistics & MathOnsite - Capital OneMediumData ScientistDetermine Country with Most 'Sunny' Days
This question evaluates proficiency in data aggregation, grouping, and filtering to summarize categorical time-series records using SQL or equivalent Python data-manipulation tools, and it belongs to the Data Manipulation domain with an emphasis on practical application of query and aggregation techniques.
Data Manipulation (SQL/Python)Onsite - Capital OneMediumData ScientistDetermine Factors Influencing Airline Flight Delays Statistically
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Determine Factors Influencing Airline Flight Delays Statistically states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathTechnical Screen - Capital OneMediumData ScientistDetermine Optimal Budget Allocation for Maximum Profit
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 Optimal Budget Allocation for Maximum Profit states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - Capital OneMediumData ScientistDetermine Rent Price Factors for Multi-Family Apartment 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 Determine Rent Price Factors for Multi-Family Apartment Profitability states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - Capital OneMediumData ScientistDetermine intersection dimension of two 4D subspaces
This question tests the application of the Grassmann Dimension Formula to determine the possible dimensions of the intersection of two subspaces within a larger vector space.
Statistics & MathTechnical ScreenLinear AlgebraVector SpacesSubspaces - Capital OneEasyData ScientistDiagnose Flight Delays and Burger Launch
This question evaluates your ability to diagnose statistical modeling trade-offs on noisy operational flight data and perform a structured financial break-even analysis for a new lounge product launch.
Analytics & ExperimentationOnsiteregressioncausal-inferencebreak-even-analysis - Capital OneMediumData ScientistDiagnose Multicollinearity in Flight Delay Prediction Model
Evaluates flight-delay modeling, data quality checks, and multicollinearity diagnosis. Strong answers identify leakage and timestamp issues, choose classification or regression based on the target, diagnose VIF and correlated features, and mitigate multicollinearity based on prediction or interpretation goals.
Machine LearningOnsite - Capital OneHardData ScientistDiagnose and fix a flight-delay modeling setup
This question evaluates proficiency in statistical modeling for binary outcomes, including concepts such as appropriate model choice and link functions, categorical encoding, ETL-driven data quality issues, multicollinearity diagnostics (e.g.
Statistics & MathOnsite - Capital OneHardData ScientistDiagnose profit drop via mix decomposition
The question evaluates a data scientist's competency in profit decomposition, attribution, experiment design, and diagnostic dashboarding within the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - Capital OneEasyData ScientistDifferentiate fixed and variable costs with examples
This question evaluates understanding of cost classification and unit economics—specifically the distinction between fixed and variable costs—and the competency to apply those concepts within a subscription streaming business context.
Statistics & MathHR Screen - Capital OneMediumData ScientistDiscuss Ethical Concerns of Facial-Recognition Technology Implementation
Evaluates Capital One behavioral and ethics interview readiness, including cross-functional teamwork, advanced analytical techniques, and facial-recognition login risks. Strong answers cover privacy, consent, biometric security, fairness, accessibility, compliance, governance, and customer-trust guardrails.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistDissect a failure and corrective actions
This question evaluates a candidate's competency in root-cause failure analysis, accountability, stakeholder impact quantification, postmortem documentation, and the design and verification of corrective controls relevant to a Data Scientist role.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistDraft a non-pushy follow-up email
This question evaluates written communication, professional etiquette, stakeholder management, and the ability to convey technical value succinctly in a Data Scientist hiring context.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistEstimate Default Rates Using Logistic Regression Model
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Estimate Default Rates Using Logistic Regression Model states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - Capital OneMediumData ScientistEstimate Revenue and Profitability for Share Workplace's Paid Tier
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 Estimate Revenue and Profitability for Share Workplace's Paid Tier states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - Capital OneMediumData ScientistEstimate Venmo Revenue and Boost User Engagement 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 Estimate Venmo Revenue and Boost User Engagement Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - Capital OneMediumData ScientistEvaluate Energy One's Transition to Renewable Energy Sources
Evaluates energy transition economics for moving from fossil fuels to renewables. Strong answers compare capex, fixed and variable costs, capacity, contribution margin, required MWh for target return, renewable technology trade-offs, policy incentives, reliability, and risk-adjusted recommendation criteria.
Analytics & ExperimentationOnsite
Capital One