Data Scientist Interview Questions

Showing 121–150 of 264 results

• 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
• 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
  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
• 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
• 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
• 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
• 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
• 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
• 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
• InstacartHard·Data Scientist
  Improve low R² without p‑hacking

  This question evaluates competence in statistical modeling and causal inference, covering regression diagnostics, feature engineering and interactions, appropriate error distributions and link functions, leakage detection, model selection and validation, and the trade-off between predictive accuracy and valid effect estimation.

  Machine LearningOnsite
• IntuitEasy·Data Scientist
  Build a predictive model from TurboTax sample data

  This problem tests your ability to design an end-to-end predictive modeling pipeline for tax-related data, focusing on feature engineering, handling imbalanced datasets, and selecting appropriate evaluation metrics.

  Machine LearningOnsiteClassificationFeature EngineeringImbalanced Data
• IntuitMedium·Data Scientist
  Engineer and Impute ZIP Features

  This Intuit data scientist machine learning screen evaluates feature engineering from address/ZIP data, joining external census-style datasets, high-cardinality encoding, and missing-ZIP imputation. It also tests awareness of fairness, privacy, leakage, and how to validate whether geographic features actually improve a model.

  Machine LearningTechnical Screen
• J.P. MorganMedium·Data Scientist
  Explain Overfitting and Transformer Basics

  This question tests your conceptual understanding of model generalization, the mechanics of the Transformer architecture, and the trade-offs involved in sequence modeling.

  Machine LearningTechnical ScreenDeep LearningTransformersBias-Variance Tradeoff
• J.P. MorganMedium·Data Scientist
  Test whether samples follow a binomial distribution

  This problem tests the application of goodness-of-fit testing to verify if a set of discrete observations conforms to a binomial distribution.

  Machine LearningOnsiteStatisticsHypothesis TestingProbability
• LinkedInEasy·Data Scientist
  Design a short-video recommender system

  This question assesses the ability to architect a multi-stage machine learning pipeline for a short-video feed, covering candidate generation, ranking, and performance evaluation.

  Machine LearningTechnical ScreenSystem DesignRecommendationRanking
• LinkedInMedium·Data Scientist
  Explain variance reduction in random forests

  This question tests your conceptual understanding of how Random Forests reduce the variance of base decision trees through bagging and feature randomness.

  Machine LearningTechnical ScreenEnsemble MethodsRandom ForestBias-Variance Trade-off
• LinkedInEasy·Data Scientist
  Handle imbalance, validate samples, and avoid overfitting

  This question tests your ability to design a robust machine learning pipeline for a highly imbalanced classification task, focusing on sampling strategies, metric selection, and regularization techniques to prevent overfitting.

  Machine LearningTechnical ScreenImbalanceOverfittingRegularization
• LyftHard·Data Scientist
  Develop Dynamic-Pricing Algorithm for Lyft Balancing Key Factors

  Evaluates dynamic-pricing system design for Lyft's two-sided marketplace. Strong answers separate prediction from control, include real-time demand and supply features, model elasticity and ETA response, optimize with fairness and stability constraints, and validate through simulation and experiments.

  Machine LearningTechnical Screen
• MetaMedium·Data Scientist
  Apply reinforcement learning to product decisions

  This question assesses the ability to determine the appropriate RL framework for recommendation systems, evaluate policies using offline data, and implement safety guardrails.

  Machine LearningOnsiteReinforcement LearningRecommendation SystemsOffline Evaluation
• MetaHard·Data Scientist
  Choose and compute recommender evaluation metrics

  This question evaluates competency in offline recommender-system evaluation, propensity-weighted policy comparison, classification metric interpretation, probability calibration, model selection trade-offs, and handling data leakage and network interference.

  Machine LearningHR Screen
• MetaMedium·Data Scientist
  Choose threshold under asymmetric costs

  This question evaluates cost-sensitive classification, operating-threshold selection, score calibration, drift monitoring, and experiment and guardrail design competencies within the Machine Learning domain.

  Machine LearningOnsite
• MetaHard·Data Scientist
  Deploy multi-armed bandits safely

  This question evaluates proficiency in online experimentation and sequential decision-making, covering Bayesian bandits (Thompson Sampling), safety guardrails for churn, delayed-conversion handling, traffic allocation and stopping/rollback policy design.

  Machine LearningOnsite
• MetaHard·Data Scientist
  Design Machine Learning Model for Facebook Groups Post Ranking

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Design Machine Learning Model for Facebook Groups Post Ranking states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnsite
• MetaHard·Data Scientist
  Design a Restaurant Recommendation System for Food Apps

  Meta machine learning system-design prompt on restaurant recommendations for food apps, covering retrieval and ranking models, features, cold start, implicit feedback, offline metrics, A/B testing, and marketplace guardrails.

  Machine LearningOnsite
• MetaMedium·Data Scientist
  Design an ad recommendation and ranking system

  This question evaluates end-to-end ad recommendation and ranking design skills, covering machine learning modeling, recommender systems, causal and counterfactual evaluation, online learning, and production ML engineering within the Machine Learning domain, at a high-level system-design and modeling abstraction.

  Machine LearningOnsite