practice a little every day, paws crossed ♡
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 421–450 of 1971 results
- Capital OneMediumData ScientistEvaluate Factors Before Renewing TV-Series Contracts
Capital One analytics case on TV-series renewal and sale decisions, covering cost-revenue profit, qualitative factors, viewer value, opportunity cost, NPV, profitability actions, and keep-versus-sell economics.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistEvaluate Financial Feasibility of Ride-Sharing Service
Evaluates ride-sharing financial feasibility with pricing, capacity, driver costs, fixed costs, and competitive strategy. Strong answers compute driver capacity, daily revenue, driver cost, fixed cost, and profit, then assess pricing and utilization levers with marketplace health guardrails.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistEvaluate Groupon's Impact on Restaurant's Profitability and Strategy
Evaluates restaurant profitability from a Groupon-style voucher partnership under discount, commission, capacity, and incrementality assumptions. Strong answers model contribution margin, cannibalization, repeat value, and strategic deal terms.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistEvaluate Growth and Pricing for a Grocery Delivery Startup
Evaluate a grocery delivery platform's unit economics across two metro markets, construct an end-to-end marketplace metric framework, and determine whether a proposed dynamic delivery fee increases contribution profit.
Analytics & ExperimentationTechnical Screenunit-economicsmarketplace-analyticspricing-strategy - Capital OneMediumData ScientistEvaluate Joint Campaign Strategies for Credit-Card Growth
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 Joint Campaign Strategies for Credit-Card Growth states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - Capital OneMediumData ScientistEvaluate Key Metrics for Capital One Ad Campaign
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 Key Metrics for Capital One Ad Campaign states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - Capital OneMediumData ScientistEvaluate Models for Credit-Risk Scoring at Capital One
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Evaluate Models for Credit-Risk Scoring at Capital One states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - Capital OneMediumData ScientistEvaluate OutlierHandler Class for Code Quality and Testing
Evaluates code-review judgment for OutlierHandler and imputer preprocessing classes in ML pipelines. Strong answers explain fit-transform separation, leakage prevention, API quality, edge cases, and testing.
Machine LearningOnsite - Capital OneMediumData ScientistEvaluate Python Class Design in Data Pipeline
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Evaluate Python Class Design in Data Pipeline states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - Capital OneMediumData ScientistEvaluate ROI and payback for renewables
This question evaluates competency in financial modeling and quantitative analysis for utility-scale renewable projects, testing skills in ROI and payback calculation, unit economics, capacity constraint analysis, sensitivity analysis, and scenario comparison.
Statistics & MathTechnical Screen - Capital OneMediumData ScientistEvaluate Renewable Investment Factors for Government Electricity Supply
Evaluates renewable energy investment decision-making for a utility selling mainly to government buyers. Strong answers assess project economics, technology maturity, reliability, regulation, procurement requirements, contract risk, incentives, and portfolio fit before making an executive recommendation.
Behavioral & LeadershipTechnical Screen - Capital OneMediumData ScientistEvaluate a credit-card acquisition partnership
This question evaluates cohort-level unit economics, discounted cash-flow (NPV) modeling, and sensitivity/break-even analysis related to customer acquisition and credit-card portfolio economics.
Analytics & ExperimentationOnsite - Capital OneHardData ScientistEvaluate launching a vegan burger
Capital One data-scientist technical-screen case: a rival launched a hit vegan burger -- should your fast-food chain add one, and how would you test it? It exercises pricing and willingness-to-pay/elasticity, operational readiness and capacity, cannibalization vs. incrementality, marketing and labeling, success metrics with guardrails, and a matched store-level RCT (difference-in-differences, CUPED, power) feeding an explicit go/no-go rule.
Analytics & ExperimentationTechnical Screen - Capital OneHardData ScientistEvaluate merchant partnership for high-value customers
This question evaluates skills in customer lifetime value modeling, marketing experiment design, acquisition economics, funnel and credit-risk analysis, fraud and operational-cost assessment, and measurement planning for partnership decisions.
Analytics & ExperimentationTechnical Screen - Capital OneMediumData ScientistEvaluate payback and sensitivity for solar vs ethanol pilots
This question evaluates quantitative financial modeling and sensitivity analysis skills—specifically expected-value computation, simple payback estimation, and scenario-based risk quantification for renewable-energy pilots.
Statistics & MathTechnical Screen - Capital OneMediumData ScientistExplain App Growth Strategy and Key Performance Metrics
Evaluates app growth strategy communication in an analytics interview for consumer digital products. Strong answers explain the app, monetization, core metrics, roadmap, first experiment, ROI, and trade-offs for a manager audience.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistExplain MSE vs MAE, AUC, and imbalance handling
This question evaluates a candidate's understanding of regression loss trade-offs (MAE vs MSE), classification evaluation metrics under severe class imbalance (ROC-AUC vs PR-AUC), neural network output activation and loss dynamics, and practical techniques for imbalance mitigation and probability-threshold selection in binary classification.
Machine LearningHR Screen - Capital OneMediumData ScientistExplain Shell Script Line-by-Line for Data Science Workflows
This question evaluates familiarity with shell scripting for reproducible data-science workflows, Python object-oriented design including transformer patterns (fit/transform separation), outlier detection and imputation strategies, code-style critique, and unit testing competency.
Coding & AlgorithmsTechnical Screen - Capital OneMediumData ScientistExplain Shell Script for Python Virtual Environment Setup
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Explain Shell Script for Python Virtual Environment Setup states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - Capital OneMediumData ScientistExplain an ML Project from Model Development Through Deployment
This question assesses your ability to articulate the end-to-end lifecycle of an ML project, focusing on the bridge between technical modeling choices and real-world business impact.
ML System DesignTechnical ScreenML LifecycleModel DeploymentSystem Design - Capital OneMediumData ScientistExplain helping-others trade-offs with evidence
This question evaluates a Data Scientist's leadership, collaboration, prioritization, stakeholder management, documentation practices, and ability to quantify impact and analyze trade-offs, framed in the Behavioral & Leadership category.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistExplain how helping others helped you
This question evaluates behavioral and leadership competencies—proactive collaboration, independent initiative, stakeholder management, and the ability to quantify return on investment in a Data Scientist context within the Behavioral & Leadership domain.
Behavioral & LeadershipTechnical Screen - Capital OneEasyData ScientistFind minimal a for PSD of 3x3 matrix
This question evaluates understanding of linear algebra concepts such as eigenvalues, matrix definiteness, and spectral properties by asking for the minimal real parameter a that makes a symmetric 3×3 matrix with ones on the diagonal and constant off-diagonal entries positive semidefinite.
Statistics & MathTechnical Screen - Capital OneMediumData ScientistFind top category per region in Aug 2025
This question evaluates proficiency in SQL data manipulation skills such as joining related tables, aggregating revenue, filtering by date and status, and implementing deterministic tie-breaking for categories.
Data Manipulation (SQL/Python)Online Assessment - Capital OneMediumData ScientistFix dash dates and aggregate watch time
This question evaluates data-cleaning and aggregation competencies, focusing on handling nonstandard missing-date markers, forward-filling in spreadsheets, correct date-range filtering, pivot-table summarization, and concise pandas grouping and aggregation.
Data Manipulation (SQL/Python)Online Assessment - Capital OneMediumData ScientistFix failing tests and refactor code
This question evaluates a candidate's ability to debug, test, and refactor Python code with attention to state management, mutable default arguments, deterministic unit testing, API design, and type-safety.
Coding & AlgorithmsTechnical Screen - Capital OneEasyData ScientistGraph WTP vs content and explain cap
This question evaluates understanding of piecewise linear functions, marginal analysis, and the economic interpretation of willingness-to-pay, within the Statistics & Math domain for a Data Scientist role.
Statistics & MathHR Screen - Capital OneMediumData ScientistHandle an irate passenger after flight delay
This question evaluates crisis communication, conflict de-escalation, resource-constrained decision-making, cross-functional coordination, and metrics definition for operational recovery, presented for a Data Scientist and categorized under Behavioral & Leadership.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistHandle extended team-matching uncertainty
This question evaluates a Data Scientist candidate's stakeholder management, escalation judgment, prioritization, and communication strategy when managing uncertainty across multiple recruiting teams.
Behavioral & LeadershipOnsite - Capital OneEasyData ScientistHandle priority changes and unclear deadlines
This set of behavioral questions evaluates leadership, prioritization, stakeholder communication, risk and uncertainty management, and adaptability skills for a Data Scientist role.
Behavioral & LeadershipTechnical Screen
Capital One