Data Scientist Interview Questions

Showing 841–870 of 1971 results

• 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
• HHeadlandsMedium·Data Scientist
  What Cross-Validation Reduces: Bias, Variance, Type I or Type II Error

  This question tests your conceptual understanding of how k-fold cross-validation affects the generalization error and how the choice of 'k' influences the trade-off between bias and variance in model performance estimation.

  Machine LearningOnline AssessmentModel EvaluationCross-ValidationStatistics
• HHeadlandsMedium·Data Scientist
  t-Stat With an Added Orthogonal Regressor, Live Sharpe Check, and Absolute-Loss Estimator

  Solve three core quantitative problems covering OLS regression mechanics under orthogonal regressors, statistical significance of backtested Sharpe ratios in live deployment, and loss-function minimization for L1 error.

  Statistics & MathOnline AssessmentEconometricsSharpe RatioLinear Regression
• 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 Smaller Numbers with Only Zero and One in Base Four

  Count the number of positive integers strictly less than n whose base-4 representation consists only of the digits 0 and 1.

  Coding & AlgorithmsOnline AssessmentMathBit ManipulationDigit DP
• 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
• HHuluMedium·Data Scientist
  Design and Monitor a Multi-Country Parameter Experiment

  This question tests the ability to design an A/B test across multiple regions while accounting for sample size requirements, variance reduction techniques, and sequential monitoring protocols.

  Analytics & ExperimentationTechnical ScreenA/B TestingPower AnalysisCUPED
• HHuluMedium·Data Scientist
  Interpret a Streaming Regression Without Overstating Causality

  This problem tests your ability to interpret multivariate regression results for product stakeholders, specifically focusing on distinguishing correlation from causation and identifying risks like collider bias.

  Analytics & ExperimentationTechnical ScreenRegressionCausal InferenceExperimental Design
• IBMEasy·Data Scientist
  Design a lead-scoring model

  Design an end-to-end lead-scoring model for a marketing/growth data science team: define the label and scoring time, engineer leakage-safe features, compare a logistic-regression baseline with gradient-boosted trees, and evaluate with capacity-aware ranking metrics. The question also probes multicollinearity, model interpretability (SHAP/reason codes), routing-threshold design, and operational risks like selection bias, class imbalance, fairness, and drift.

  Machine LearningTechnical Screen
• IBMEasy·Data Scientist
  Estimate free-trial conversion probability

  This question evaluates a data scientist's competency in cohort definition, censored-data handling, segmentation and confounding assessment, and uncertainty quantification for measuring free-trial to paid conversion, and it belongs to the Analytics & Experimentation domain within data science.

  Analytics & ExperimentationTechnical Screen
• IBMEasy·Data Scientist
  How do you detect and fix multicollinearity?

  This question evaluates understanding of multicollinearity and related competencies in regression diagnostics, feature engineering, model interpretability, and the trade-offs between coefficient inference and predictive performance.

  Statistics & MathTechnical Screen
• IBMEasy·Data Scientist
  Write SQL to compute max and min marks

  This question evaluates a candidate's ability to perform basic data aggregation in SQL, specifically computing summary statistics from a single table. It is commonly asked to assess competence in data manipulation and query result shaping within the Data Manipulation (SQL/Python) domain, focusing on practical application of SQL aggregation rather than purely conceptual theory.

  Data Manipulation (SQL/Python)Online Assessment
• ImcMedium·Data Scientist
  Adapting a Research Approach Midstream

  This question assesses the candidate's ability to balance technical rigor with business agility, specifically focusing on communication skills, professional resilience, and stakeholder management under pressure.

  Behavioral & LeadershipHR ScreenCommunicationStakeholder ManagementAdaptability
• ImcMedium·Data Scientist
  Compute gambler’s ruin probabilities and hitting times

  This question tests your ability to model a discrete random walk as a Markov chain, derive the probability of reaching an absorption state, and calculate the expected time to reach that state.

  Machine LearningOnsiteProbabilityMarkov ChainsStochastic Processes
• ImcMedium·Data Scientist
  Explain linear regression and Transformer fundamentals

  This question tests your ability to explain the theoretical foundations of linear regression, including its probabilistic interpretation, and your technical depth regarding the mechanics of the Transformer architecture.

  Machine LearningOnsiteLinear RegressionTransformersSelf-Attention
• ImcEasy·Data Scientist
  Find valid range of q for outcome probabilities

  This question tests the ability to solve a system of linear inequalities derived from probability constraints, requiring an understanding of basic arbitrage-free pricing principles.

  Software Engineering FundamentalsTechnical ScreenProbabilityInequalitiesFinance
• ImcMedium·Data Scientist
  Managing a Research Project End to End

  This question assesses a candidate's ability to structure open-ended research, manage stakeholder expectations, and maintain scientific rigor while ensuring the final output is actionable for a business audience.

  Behavioral & LeadershipHR ScreenProject ManagementCommunicationAmbiguity
• ImcMedium·Data Scientist
  Motivation for Quantitative Research

  This question assesses the candidate's ability to articulate their intellectual passion for quantitative research by connecting personal motivations to the practical realities of high-frequency trading or data-driven decision-making.

  Behavioral & LeadershipHR ScreenBehavioralQuantitativeResearch
• ImcMedium·Data Scientist
  Run EDA and train models while preventing overfitting

  This problem tests your end-to-end machine learning pipeline skills, focusing on tabular regression, feature engineering, validation strategies, and regularization techniques.

  Machine LearningOnsiteRegressionOverfittingEDA
• ImcMedium·Data Scientist
  Solve a constrained problem using KKT conditions

  This question tests the ability to solve a convex optimization problem under inequality constraints using KKT conditions and the identification of active versus inactive constraints.

  Machine LearningOnsiteOptimizationKKTCalculus
• InstacartMedium·Data Scientist
  Aggregate weekly revenue and attribute 4% drop

  This question evaluates a candidate's ability to perform time-based revenue aggregation, filtering, percent-change calculation, and geo-level attribution using SQL or Python, with attention to ISO-week alignment and revenue computation.

  Data Manipulation (SQL/Python)Technical Screen
• InstacartHard·Data Scientist
  Analyze A/B test with revenue–cost tradeoffs

  This question evaluates a data scientist's skills in experimental design and statistical inference for A/B testing, revenue-impact modeling with skewed and zero-inflated metrics, segmentation for heterogeneous treatment effects, and constrained optimization to balance contribution and operational risk.

  Analytics & ExperimentationOnsite
• InstacartMedium·Data Scientist
  Calculate Weekly Revenue and Order Count for Standard Deliveries

  This question evaluates proficiency in time-based aggregations and filtering on order-level transactional data to compute weekly revenue and order counts specifically for standard delivery orders.

  Data Manipulation (SQL/Python)Technical Screen
• InstacartHard·Data Scientist
  Choose tests under non‑normal, unequal variance

  This question tests the ability to select and justify statistical inference methods for metrics that violate standard parametric assumptions, specifically when dealing with heavy-tailed, heteroskedastic, and zero-inflated distributions.

  Statistics & MathOnsiteStatisticsA/B TestingInference
• InstacartHard·Data Scientist
  Compute duration and stopping rules correctly

  This question evaluates a data scientist's competency in experimental design and applied statistics, specifically power and sample‑size calculations, accounting for intra‑household clustering and design effects, sequential monitoring for peeking control, and variance‑reduction or stratified randomization to handle day‑of‑week seasonality.

  Analytics & ExperimentationOnsite
• InstacartEasy·Data Scientist
  Design a pricing experiment with network effects

  This question evaluates experimental design and causal inference competencies for a two-sided marketplace pricing change, including handling network effects/interference, choice of unit of randomization and treatment markets, metric selection and diagnostics, power/MDE estimation, and operational risk assessment within the Analytics & Experimentation domain for a Data Scientist role. It is commonly asked to assess practical application of randomized and quasi-experimental methods across heterogeneous geographies and operational constraints, requiring both hands-on experimental design skills and conceptual understanding of bias, spillovers, and diagnostic metrics.

  Analytics & ExperimentationTechnical Screen
• InstacartHard·Data Scientist
  Design and Interpret an A/B Test

  This question tests the ability to design a statistically rigorous A/B test, calculate sample sizes based on effect thresholds, and account for real-world constraints like seasonality and multiple comparisons.

  Analytics & ExperimentationOnsiteA/B TestingPower AnalysisStatistics
• InstacartMedium·Data Scientist
  Diagnose and fix low conversion rigorously

  This question evaluates a Data Scientist's competency in analytics and experimentation, covering instrumentation validation, funnel and segmentation analysis, causal inference methods (holdouts/DiD), system reliability diagnostics, statistical power and sample-size estimation, and the ability to synthesize findings into executive-level impact statements. It is commonly asked to determine whether a candidate can distinguish measurement or product regressions from marketing-mix shifts and to demonstrate both conceptual understanding of causal inference and practical application of data querying, metric definition, and experiment design in the Analytics & Experimentation domain.

  Analytics & ExperimentationHR Screen