Data Scientist Interview Questions

Showing 451–480 of 1076 results

• GoogleMedium·Data Scientist
  Infer distribution and choose robust statistics

  This problem tests the ability to diagnose non-normal data distributions (specifically zero-inflated, heavy-tailed data) and select appropriate statistical metrics for hypothesis testing.

  Statistics & MathTechnical ScreenStatisticsData DistributionsHypothesis Testing
• GoogleMedium·Data Scientist
  Match payments to invoices by memo or amount

  This problem tests string parsing, hash-map based data indexing, and multi-criteria reconciliation logic (fuzzy matching via IDs vs. exact matching via amount).

  Coding & AlgorithmsTechnical ScreenHashingStringsData Reconciliation
• GoogleMedium·Data Scientist
  Measure Bird Species Segregation

  This question evaluates spatial statistical reasoning, ecological data analysis, and the ability to formulate hypotheses, choose appropriate segregation metrics, and account for confounders and sampling bias.

  Statistics & MathTechnical Screen
• GoogleHard·Data Scientist
  Measure outage impact; choose fix vs build

  This question evaluates instrumentation design, telemetry analysis, causal impact estimation, failure deduplication, and decision-making under uncertainty for a Data Scientist, testing competencies in analytics, experimentation, and product-metric prioritization.

  Analytics & ExperimentationTechnical Screen
• GoogleMedium·Data Scientist
  Minimize L2, L1, and quantile losses

  This question evaluates understanding of statistical estimators and optimization principles for L2, L1, and tilted absolute (quantile) loss functions, including use of derivatives and subgradients, order-statistics, algorithmic complexity, and robustness considerations.

  Coding & AlgorithmsTechnical Screen
• GoogleHard·Data Scientist
  Model Soccer Shot Conversion

  This problem tests the ability to design an end-to-end predictive model for sports events, focusing on feature engineering, handling spatial data sparsity, and evaluating probabilistic outputs.

  Machine LearningTechnical ScreenLogistic RegressionModel CalibrationFeature Engineering
• GoogleMedium·Data Scientist
  Narrow a confidence interval for a mean

  This question evaluates understanding of confidence intervals, the relationship between sample size and interval width, variance-reduction techniques, the impact of changing confidence levels, and the ability to communicate statistical risk and design-based trade-offs.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Normalize Columns in Binomial Matrix Efficiently

  This question evaluates proficiency in matrix manipulation, handling of binomial random samples, column normalization, and algorithmic efficiency for processing large numerical arrays.

  Coding & AlgorithmsTechnical Screen
• GoogleHard·Data Scientist
  Predict and act on contract renewal risk

  This question evaluates competency in applied machine learning for enterprise churn prediction, testing skills in problem framing, feature engineering, model selection, data hygiene, evaluation metrics, and decisioning within the Machine Learning domain.

  Machine LearningTechnical Screen
• GoogleMedium·Data Scientist
  Prove OLS invariance to linear transforms

  This question evaluates understanding of linear regression invariance under invertible linear transformations, multivariate linear algebra, and the impact of regularization (ridge and lasso) on coefficient and prediction stability in the Statistics & Math domain.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Test a coefficient and explain t-distribution

  This question evaluates a candidate's competence in statistical inference for linear regression—specifically hypothesis testing for OLS coefficients, construction of t-statistics, estimation of standard errors, and the role of estimated error variance—within the Statistics & Math domain for Data Scientist roles.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Test if one value comes from N(μ,σ²)

  This question tests the ability to perform statistical hypothesis testing on a single observation, evaluate the limitations of small sample sizes, and explain the trade-offs between Type I and Type II errors.

  Statistics & MathTechnical ScreenStatisticsHypothesis TestingNormal Distribution
• GoogleMedium·Data Scientist
  Write SQL/Python for messy event data

  This question evaluates SQL and pandas proficiency for event-level data manipulation, including time zone–aware local date conversion, session-based deduplication, cohort and conversion metric calculation, and basic experiment sanity checking via a sample-ratio-mismatch p-value.

  Data Manipulation (SQL/Python)Technical Screen
• GGrindrMedium·Data Scientist
  How would you evaluate causal lift and a paywall change?

  A two-part Grindr Data Scientist technical screen on analytics and experimentation. Part 1 designs an advertiser-level causal study for a new ad-optimization product when the eligible advertiser pool is too small, covering MDE/power, CUPED, matched-pair and panel designs, interference, and heterogeneous effects. Part 2 evaluates lowering a free-user paywall from 100 to 80 profiles, balancing short-term conversion against long-term LTV, retention, ad revenue, and one-sided-triggering selection bias.

  Analytics & ExperimentationTechnical Screen
• GGsaMedium·Data Scientist
  Critique a CNN-Based Bridge Safety Proposal

  This question tests the ability to identify flaws in a computer vision project, focusing on data validity, label sparsity, adversarial robustness, and the practical limitations of predictive modeling in structural engineering.

  Machine LearningTechnical ScreenComputer VisionAdversarial MLData Ethics
• GGsaHard·Data Scientist
  Optimize Matrix-Chain Multiplication

  This problem tests your ability to solve a classic dynamic programming challenge, focusing on finding the optimal order of matrix multiplications to minimize the total number of scalar operations.

  Coding & AlgorithmsTechnical ScreenDynamic ProgrammingAlgorithmsOptimization
• GGsaHard·Data Scientist
  Predict Stock Returns from Sentiment and Market Data

  This problem tests the ability to design an end-to-end predictive pipeline for stock returns, focusing on time-series integrity, feature engineering, and robust evaluation metrics.

  Machine LearningTechnical ScreenFinancial ModelingTime SeriesData Leakage
• GustoMedium·Data Scientist
  Build Churn Prediction and Survival Models

  This problem tests the ability to design an end-to-end churn prediction pipeline, specifically focusing on the distinction between binary classification and survival analysis in the context of customer retention.

  Machine LearningTechnical ScreenChurn PredictionSurvival AnalysisFeature Engineering
• GustoMedium·Data Scientist
  Calculate Net Pay Change for Q1 2023 Decreases

  This question evaluates a candidate's data-manipulation competency in SQL and Python, focusing on computing net pay, aggregating over a calendar quarter, and measuring percentage change between recent pay periods.

  Data Manipulation (SQL/Python)Technical Screen
• GustoMedium·Data Scientist
  Compute Parking Clustering Probability and P-Value

  This question tests your ability to calculate exact combinatorial probabilities under a null hypothesis and define a p-value for a spatial clustering test.

  Statistics & MathTechnical ScreenProbabilityCombinatoricsHypothesis Testing
• GustoEasy·Data Scientist
  Compute p-values for 2 variants vs control

  This question evaluates a candidate's competence in statistical hypothesis testing and experiment analysis, focusing on A/B/n comparison of conversion rates, p-value computation, confidence interval estimation for lift, multiple-testing considerations, and interpretation of results.

  Analytics & ExperimentationTechnical Screen
• GustoMedium·Data Scientist
  Describe Your Analytical Experience and Cross-Functional Collaboration.

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Your Analytical Experience and Cross-Functional Collaboration. states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipTechnical Screen
• GustoMedium·Data Scientist
  How do you test two variants vs control?

  This question evaluates proficiency in A/B/n experimentation, hypothesis testing for binomial proportions, multiple-comparison correction methods, confidence interval estimation for conversion uplift, and experiment-related decision-making in the Analytics & Experimentation domain at an applied/intermediate statistical-analysis level.

  Analytics & ExperimentationTechnical Screen
• GustoMedium·Data Scientist
  Identify Growth Opportunities for New Payroll Feature Launch

  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 Identify Growth Opportunities for New Payroll Feature Launch states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• GustoMedium·Data Scientist
  Investigate Causes of Increased Payroll Processing Time

  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 Investigate Causes of Increased Payroll Processing Time states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• HHeadlandsMedium·Data Scientist
  Compare Candy-Color Guessing Experiments with Different Sample Sizes

  Evaluate and compare evidence from two binomial experiments with different sample sizes and hit rates against a random guessing null hypothesis.

  Statistics & MathTechnical Screenstatisticshypothesis-testingbinomial-distribution
• Hudson River TradingMedium·Data Scientist
  Compare Mean and Median for a Distribution with a Point Mass

  This question tests the theoretical and empirical comparison between the sample mean and sample median on a mixture distribution containing a discrete point mass and a Gaussian component.

  Statistics & MathTechnical Screenstatisticsprobabilityestimation
• Hudson River TradingMedium·Data Scientist
  Count inversions in a permutation

  This question evaluates understanding of inversion counting in permutations, testing algorithmic problem-solving, complexity analysis, and data-structure reasoning about pairwise order relations.

  Coding & AlgorithmsTechnical Screen
• Hudson River TradingMedium·Data Scientist
  Derive expected inversions and mean distribution

  This problem tests your understanding of linearity of expectation and variance properties applied to permutation statistics, as well as the ability to design a simulation to verify analytical derivations.

  Machine LearningTechnical ScreenProbabilityStatisticsExpectation
• Hudson River TradingMedium·Data Scientist
  Solve Dice Probability and Matrix Questions

  This problem tests probability distributions over modular arithmetic and the identification of properties of doubly stochastic matrices.

  Machine LearningTechnical ScreenProbabilityLinear AlgebraMatrices