Data Scientist Interview Questions

Showing 811–840 of 1971 results

• 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
  Reading a Regression Slope: P-values, Intervals, and Causality

  Use a fitted slope from a one-predictor regression to explain precisely what a p-value and a confidence interval mean, what assumptions they rest on, how confidence and prediction intervals differ, and why significance is not causation. Tests statistical precision under interview pressure.

  Statistics & MathOnsitestatisticslinear-regressionhypothesis-testing
• GoogleMedium·Data Scientist
  Reflect on a failed decision and redo it

  This question evaluates decision-making, ownership, risk assessment, stakeholder management, product impact analysis, and post‑mortem reflection competencies relevant to data scientists.

  Behavioral & LeadershipOnsite
• GoogleMedium·Data Scientist
  Resolve Team Disagreement on Off-Site Activity Choice

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Resolve Team Disagreement on Off-Site Activity Choice states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipOnsite
• GoogleHard·Data Scientist
  Resolve conflict on trust versus growth priorities

  This question evaluates a candidate's competency in conflict management, stakeholder mapping and alignment, risk assessment for content safety, reversible experiment and phased rollout design, and respectful escalation under time pressure.

  Behavioral & LeadershipOnsite
• GoogleEasy·Data Scientist
  Select MOST/LEAST appropriate actions (SJT)

  This question evaluates situational judgment, ethical decision-making, and adherence to data confidentiality and respectful workplace policies relevant to a Data Scientist role.

  Behavioral & LeadershipOnsite
• GoogleMedium·Data Scientist
  Simulate Coin Flips to Determine Fairness via Empirical Distribution

  This question evaluates a candidate's ability to perform simulation-based hypothesis testing by implementing Monte Carlo sampling and computing an empirical two-sided p-value from observed coin-flip counts, emphasizing statistical computation and numerical accuracy.

  Coding & AlgorithmsOnsite
• GoogleMedium·Data Scientist
  Simulate Uniform(0,1) from random bits

  This Coding & Algorithms question for a Data Scientist evaluates understanding of randomized algorithms, bit-level representation of real numbers, and probabilistic error and time-complexity analysis, situated at an algorithmic and mathematical abstraction level that requires both conceptual design and quantitative reasoning.

  Coding & AlgorithmsOnsite
• 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
  Understand Simpson's Paradox with Simple Examples

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Understand Simpson's Paradox with Simple Examples states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnsite
• 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
  Evaluate Dating App Product Changes

  This question tests the ability to design an A/B test for a two-sided marketplace, focusing on network effects, cannibalization of metrics, and the selection of hold-out groups to measure long-term user behavior.

  Analytics & ExperimentationOnsiteA/B TestingMarketplace AnalyticsCausal Inference
• 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
  Build a Churn Prediction Model

  This question tests the ability to design an end-to-end churn prediction pipeline, focusing on temporal data integrity, class imbalance, and actionable evaluation metrics for business stakeholders.

  Machine LearningOnsiteChurnFeature EngineeringImbalanced Data
• 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
  Define Churn and Design Onboarding Experiment

  This question tests the ability to define business-relevant churn metrics for a SaaS platform and design a controlled experiment to optimize user onboarding.

  Analytics & ExperimentationOnsiteAnalyticsExperimentationMetrics
• 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
• HHBOHard·Data Scientist
  Define scalable train/validation for churn

  This question assesses the ability to design an end-to-end churn prediction pipeline, focusing on temporal integrity, handling class imbalance, and ensuring robust production deployment.

  Machine LearningOnline AssessmentChurnSystem DesignData Leakage
• HHBOHard·Data Scientist
  Design an A/B for rare cancellations

  This question evaluates a data scientist's competency in experimental design, rare-event power analysis, precise metric and guardrail definitions, variance-reduction covariate adjustment, sequential-testing decisions, monitoring/dashboard specification, and interpretation of heterogeneous treatment effects.

  Analytics & ExperimentationOnline Assessment
• HHeadlandsMedium·Data Scientist
  C++ Sliding-Window Mean of a Float Array With Inf, -Inf and NaN Handling

  Implement an O(n) sliding-window mean over a 32-bit float array containing IEEE-754 special values (+inf, -inf, NaN) without poisoning subsequent windows or suffering numerical drift.

  Software Engineering FundamentalsOnline Assessmentcppsliding-windowieee-754