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Statistics & Math interview questions

439 Statistics & Math questions from 87 companies, with hints, runnable code and solution walkthroughs.

Questions439

119 easy239 medium81 hard

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Statistics & Math interview questions by the numbers

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Katkat has 439 Statistics & Math interview questions from 87 companies; the most come from Meta (11%), Capital One (9%) and SIG (Susquehanna) (8%). By difficulty: 27% easy, 54% medium and 18% hard. The most common round is Technical Screen (50%). Frequent concepts include Statistics, Probability, Inference and Hypothesis Testing.

Companies (87)

Difficulty

  • Easy27% · 119
  • Medium54% · 239
  • Hard18% · 81

Interview rounds

  • Technical Screen50% · 218
  • Onsite28% · 124
  • Online Assessment18% · 78
  • HR Screen4% · 18
  • Other0% · 1

Roles

  • Data Scientist78% · 344
  • Software Engineer10% · 43
  • Quantitative Researcher6% · 26
  • Quantitative Trader3% · 14
  • Machine Learning Engineer2% · 10

Frequent concepts

  • Statistics14% · 61
  • Probability11% · 49
  • Inference3% · 15
  • Hypothesis Testing3% · 13
  • probability3% · 13
  • statistics3% · 11

Showing 31–60 of 439 results

  • Prove that ordinary training R-squared cannot decrease when adding a feature, while adjusted and test-set R-squared can.

    C3 AIData ScientistStatistics & MathOnline Assessment
  • Tests a candidate's mathematical understanding of the Gaussian distribution, key theoretical properties such as affine closure and the Central Limit Theorem, and the ability to diagnose and handle non-normal distributions in real-world modeling.

    C3 AIData ScientistStatistics & MathTechnical Screen
  • This question evaluates proficiency in statistical inference for A/B testing—specifically two-proportion z-tests, construction of confidence intervals with unpooled standard errors, sample-size and power calculations, and multiple-comparison correction via Bonferroni.

    CVS HealthData ScientistStatistics & MathOnline Assessment
  • This question evaluates a candidate's understanding of statistical inference, hypothesis testing, and robust test selection, focusing on p-value interpretation and choosing between paired vs independent and parametric vs rank-based tests.

    CVS HealthData ScientistStatistics & MathTechnical Screen
  • Evaluates annual profit calculation for a credit-card portfolio with monthly and annual revenue and cost components. Strong answers annualize figures, compute per-card profit, and scale to portfolio profit.

    Capital OneData ScientistStatistics & MathHR Screen
  • Evaluates incremental customer calculations for partnership marketing spend justification. Strong answers derive break-even customers for fixed spend and required volume for variable-cost campaigns against a profit target.

    Capital OneData ScientistStatistics & MathHR Screen
  • A Capital One data-scientist technical screen on break-even and unit economics for a 30%-off Rent-a-Home promotion. You set up and solve the break-even equation for incremental new customers, size it against the 2M cardholder base, then re-solve under spend-uplift assumptions and explain why the required count nearly doubles.

    Capital OneData ScientistStatistics & MathTechnical Screen
  • This question evaluates a data scientist's quantitative reasoning about unit economics and financial metrics, including understanding fixed versus variable costs and computing profit margin as a percentage.

    Capital OneData ScientistStatistics & MathTechnical Screen
  • This question evaluates understanding of unit economics, break-even analysis, and pricing-model effects on profitability by requiring computation of revenue, variable and fixed costs, and profit under per-GB and two-tier pricing scenarios.

    Capital OneData ScientistStatistics & MathTechnical Screen
  • This question evaluates understanding of expected value, weighted averages, and profit-margin analysis within the Statistics & Math domain for a Data Scientist role. It is commonly asked to probe quantitative reasoning about pricing and cost trade-offs, and it tests practical application of arithmetic and scenario-based profit modeling rather than abstract theoretical concepts.

    Capital OneData ScientistStatistics & MathTechnical Screen
  • This question tests the candidate's ability to calculate weighted averages and understand how changes in sales volume distribution (mix shift) affect the overall average profit per unit.

    Capital OneData ScientistStatistics & MathTechnical Screen
  • A Capital One data science technical screen case on restaurant unit economics with a Groupon-style voucher. You compute baseline daily profit from a 20-table day, derive the break-even check for a voucher table under a $30-for-$15 offer with a 40% commission, profit a mixed 25-table day, and decompose the profit change into traffic, check size and voucher cost. Full worked arithmetic, a sensitivity solve, and the incrementality follow-ups an interviewer expects.

    Capital OneData ScientistStatistics & MathTechnical Screen
  • This question tests the ability to calculate expected values and determine optimal decision thresholds in a multi-stage probabilistic game.

    Capital OneData ScientistStatistics & MathOnsite
  • This question evaluates proficiency with confidence intervals, hypothesis testing for proportions and means, multiple-comparison adjustment (Bonferroni), and sample size calculations, including assessment of normal-approximation versus Wilson score methods.

    Capital OneData ScientistStatistics & MathOnline Assessment
  • This question evaluates financial modeling, unit-economics calculation, breakeven analysis, and basic risk/expected-loss estimation relevant to a Data Scientist, testing competency in statistics & math and algebraic derivation within financial analysis.

    Capital OneData ScientistStatistics & MathOnsite
  • This question evaluates numerical proficiency in financial arithmetic, unit-aware quantitative reasoning, and per-customer business-metric computation within a loyalty program context.

    Capital OneData ScientistStatistics & MathTechnical Screen
  • This question evaluates financial modeling, expected-value decision-making, probability reasoning, and sensitivity analysis skills applied to revenue, variable and fixed cost interactions and contractual fee impacts, aimed at a Data Scientist role.

    Capital OneData ScientistStatistics & MathOnsite
  • A Capital One data scientist technical-screen case on cost accounting and profitability modeling. Candidates compute the Year-1 incremental profit of a new vegan burger line, decompose it into fixed versus variable components, derive breakeven volume, and run a ±10% sensitivity analysis.

    Capital OneData ScientistStatistics & MathTechnical Screen
  • This question evaluates a candidate's grasp of optimal stopping theory, expected value computation, and finite-horizon decision-making under uncertainty, including recognition of threshold-based policies and backward-induction reasoning relevant to data scientist roles.

    Capital OneData ScientistStatistics & MathTechnical Screen
  • This problem tests your ability to model unit economics for a subscription-based loyalty program and perform sensitivity analysis to determine the break-even point for a business intervention.

    Capital OneData ScientistStatistics & MathTechnical Screen
  • This question evaluates a candidate's ability to perform cost-volume-profit analysis and quantitative financial modeling, assessing skills in algebraic formulation of profit equations, capacity constraint evaluation, and derivation of generalized feasibility formulas.

    Capital OneData ScientistStatistics & MathTechnical Screen
  • This question evaluates a candidate's proficiency in unit economics, insurance break-even analysis, expected-value modeling, churn effects, pricing versus demand trade-offs, and basic statistical inference including MLE and confidence intervals.

    Capital OneData ScientistStatistics & MathTechnical Screen
  • 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.

    Capital OneData ScientistStatistics & MathOnsite
  • 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.

    Capital OneData ScientistStatistics & MathTechnical Screen
  • 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.

    Capital OneData ScientistStatistics & MathTechnical Screen
  • 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.

    Capital OneData ScientistStatistics & MathOnsite
  • 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.

    Capital OneData ScientistStatistics & MathHR Screen
  • 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.

    Capital OneData ScientistStatistics & MathOnsite
  • 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.

    Capital OneData ScientistStatistics & MathTechnical Screen
  • 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.

    Capital OneData ScientistStatistics & MathTechnical Screen

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