Data Scientist Interview Questions

Showing 91–120 of 344 results

• DRWEasy·Data Scientist
  Derive eigenvalues and sum for inverse matrix

  Derive eigenvalues and sum for inverse matrix evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnsite
• DRWMedium·Data Scientist
  Differentiate sample vs population standard deviation

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Differentiate sample vs population standard deviation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• DRWMedium·Data Scientist
  Solve Markov and distribution expectation problems

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Solve Markov and distribution expectation problems states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnsite
• DatabricksHard·Data Scientist
  Explain Linear Regression Assumptions

  This question tests the candidate's mastery of the Gauss-Markov theorem and the consequences of violating Ordinary Least Squares (OLS) assumptions in real-world data science workflows.

  Statistics & MathTechnical ScreenStatisticsRegressionOLS
• DatabricksEasy·Data Scientist
  Relate coefficients under linear feature transformation

  This question tests the relationship between regression coefficients when predictors undergo linear transformations, specifically focusing on scaling and centering transformations.

  Statistics & MathTechnical ScreenLinear RegressionLinear AlgebraFeature Engineering
• DatabricksHard·Data Scientist
  Test coin fairness from 560 tails in 1000 flips

  This problem evaluates hypothesis testing for proportions—formulating null and alternative hypotheses, applying the binomial-to-normal approximation, and interpreting p-values; it belongs to Statistics & Math for Data Scientist roles and is pitched at an introductory/undergraduate applied statistics level.

  Statistics & MathTechnical Screen
• DoorDashMedium·Data Scientist
  Calculate power and test duration

  This question evaluates proficiency in statistical power analysis, sample size calculation for proportions, hypothesis testing for two-sided z-tests, and practical experiment duration estimation for A/B testing.

  Statistics & MathTechnical Screen
• DoorDashMedium·Data Scientist
  Compute sample sizes and error control

  This question evaluates proficiency in experimental design and applied inferential statistics—specifically sample size calculation for means and proportions, non-inferiority testing, multiple-comparison error control, cluster-randomized design effects, and sequential monitoring boundaries—within the Statistics & Math domain for a Data Scientist role. It is commonly asked to measure the ability to apply statistical formulas and error-control principles under practical constraints, balancing power and type I error while accounting for clustering and interim looks; the assessment emphasizes practical application grounded in conceptual understanding of inferential methods.

  Statistics & MathTechnical Screen
• DoorDashHard·Data Scientist
  Define and compute retention and churn precisely

  This question evaluates a Data Scientist's competency in statistical measurement of user retention and churn, covering cohort definition, activity rules, risk-set-aware retention and churn formulas, censoring and delayed conversion handling, and survival-analysis concepts like time-to-churn, hazard, and cumulative incidence.

  Statistics & MathOnsite
• DoorDashHard·Data Scientist
  Design A/B Test to Evaluate Algorithm's Revenue Impact

  DoorDash statistics and experimentation prompt on A/B testing a recommendation algorithm's revenue impact, covering hypotheses, randomization, sample size, heavy-tailed revenue, p=0.08 interpretation, and causal alternatives.

  Statistics & MathTechnical Screen
• ExperianEasy·Data Scientist
  Calculate Expected Flips for Two Heads Coin Toss

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Expected Flips for Two Heads Coin Toss states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• FRFive RingsHard·Data Scientist
  Solve coin probability and Bayesian inference problems

  This question evaluates probabilistic reasoning, stochastic-process intuition (pattern waiting times and runs), expected-value calculations, and Bayesian parameter estimation competency.

  Statistics & MathOnline Assessment
• GleanMedium·Data Scientist
  Expectation and Variance of a Sum of Two Random Variables, Including Covariance

  Asks how to compute the expectation and variance of the sum of two random variables, in general and when they are independent. Tests derivation from definitions, the role of covariance, generalizing to weighted sums and differences, and applying the result to data-science problems such as comparing group means.

  Statistics & MathOnsite
• GleanEasy·Data Scientist
  What are sum expectation and variance?

  This question evaluates understanding of expectation, variance and covariance and the ability to reason about how sums of random variables affect their moments, including the role of covariance and the special case of independence in the Statistics & Math domain.

  Statistics & MathOnsite
• Goldman SachsEasy·Data Scientist
  Bound the Rank of BA Given Rank of AB

  Determine the set of possible values for the rank of the matrix product BA given that A and B are 4x4 real matrices and rank(AB) = 2. This question tests fundamental linear algebra inequalities involving matrix ranks and nullities.

  Statistics & MathOnline AssessmentLinear AlgebraMatrix TheoryRank Inequalities
• Goldman SachsEasy·Data Scientist
  Compute the Arc Length of a Curve

  Tests single-variable calculus fundamentals, specifically calculating the derivative, setting up the standard arc length integral, and evaluating it using substitution.

  Statistics & MathOnline Assessmentcalculusintegrationgeometry
• Goldman SachsEasy·Data Scientist
  Expected Number of Runs in Coin Tosses

  Tests understanding of linearity of expectation and indicator random variables through counting consecutive runs in a sequence of coin tosses.

  Statistics & MathOnline Assessmentprobabilityexpected-valuecombinatorics
• Goldman SachsEasy·Data Scientist
  Infer BA When AB Is the Identity

  This question tests the understanding of matrix invertibility, the properties of the identity matrix, and the fundamental differences between square and non-square matrix multiplication.

  Statistics & MathOnline AssessmentLinear AlgebraMatricesRank
• Goldman SachsEasy·Data Scientist
  Integrate a Two-Sided Exponential Density

  This question tests the ability to integrate probability density functions (PDFs) over specified intervals, specifically handling the absolute value function in symmetric distributions.

  Statistics & MathOnline AssessmentCalculusProbabilityDistributions
• GoogleMedium·Data Scientist
  Analyze Linear Regression Changes with Duplicated Observations

  Evaluates regression and hypothesis-testing interpretation under duplicated observations and large samples. Strong answers show unchanged OLS coefficients, standard-error effects, p-value meaning, and practical chi-square adjustments.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Analyze data duplication effects in linear regression

  This question evaluates understanding of OLS estimation, the effects of duplicated observations on estimator variance and inference, and the interplay between sample size, p-values, and effect-size measures within the Statistics & Math domain for a Data Scientist role.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Approximate a percentile from buckets

  This question tests the ability to interpolate values within histogram buckets to estimate a specific percentile, requiring an understanding of linear versus log-space assumptions.

  Statistics & MathTechnical ScreenStatisticsEstimationAlgorithms
• GoogleMedium·Data Scientist
  Assess Fundamental Statistics Knowledge in Data-Science Interviews

  Google data scientist statistics prompt covering MLEs for normal mean and variance, conditional density from a joint pdf, standard normal pdf and cdf, and assumptions for unbiased estimators.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Can bootstrap help reduce variance

  This question evaluates understanding of bootstrap resampling, the distinction between estimating an estimator’s variance and methods that actually reduce estimator variance, and related ensemble reasoning for a Data Scientist role in the Statistics & Math domain.

  Statistics & MathOnsite
• GoogleMedium·Data Scientist
  Derive MLEs and conditional Normal distributions

  This question evaluates proficiency in parametric inference and probability theory, specifically maximum likelihood estimation, bias of estimators, and conditioning in univariate and bivariate Normal distributions including constrained estimation of parameters.

  Statistics & MathTechnical Screen
• GoogleHard·Data Scientist
  Design human review to estimate model accuracy

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Design human review to estimate model accuracy states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnsite
• GoogleMedium·Data Scientist
  Determine Impact of New Chat-Notification on User Engagement

  Determine Impact of New Chat-Notification on User Engagement evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• GoogleEasy·Data Scientist
  Determine Normality of Single Observation with Z-Test

  Evaluates hypothesis testing for a single observation against a fully specified standard normal distribution. Strong answers state hypotheses, use z = x, define critical values and p-values, and note limitations.

  Statistics & MathTechnical Screen
• GoogleHard·Data Scientist
  Estimate Population Mean and Conversion Rate Accurately

  Evaluates core statistical inference skills across p-values, confidence intervals, standard error reduction, tail probability estimation, efficient conversion-rate sampling, and truncated-normal estimation. Strong answers state assumptions, use appropriate intervals and weights, and avoid common interpretation errors.

  Statistics & MathOnsite
• GoogleMedium·Data Scientist
  Estimate percentile from buckets

  Estimate a percentile from histogram buckets by locating the cumulative-count bucket, explaining why midpoint can be poor, interpolating within the bucket under explicit assumptions, handling log-spaced buckets and edge cases, and reporting uncertainty when buckets are coarse.

  Statistics & MathTechnical Screen