Data Scientist Interview Questions

Showing 1–30 of 152 results

• AirbnbMedium·Data Scientist
  Build and evaluate an order prediction model

  This question tests the ability to design a robust end-to-end machine learning pipeline for binary classification, focusing on preventing temporal data leakage, managing class imbalance, and aligning model thresholds with business objectives.

  Machine LearningTechnical ScreenClassificationTemporal LeakageModel Evaluation
• AmazonHard·Data Scientist
  Choose Between Fine-Tuning and RAG for Client Chatbot

  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 Choose Between Fine-Tuning and RAG for Client Chatbot states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• AmazonHard·Data Scientist
  Choose Models for Imbalanced Data and Time-Series Forecasting

  Choose Models for Imbalanced Data and Time-Series Forecasting evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• AmazonMedium·Data Scientist
  Choose regularization norms and model formulations

  This question evaluates understanding of regularization norms (L0, L1, L2, L∞), objective formulations for linear and logistic regression, and practical competencies in handling collinearity, outliers, feature scaling, and optimization difficulty.

  Machine LearningTechnical Screen
• AmazonMedium·Data Scientist
  Compare RNNs and Transformers for Long-Sequence Text Classification

  Compare RNNs and Transformers for Long-Sequence Text Classification evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• AmazonHard·Data Scientist
  Compare Random Forests vs Gradient Boosting rigorously

  This question evaluates a candidate's ability to choose and configure tree-based models (Random Forest vs. Gradient Boosted Trees), handle high-cardinality categorical features and missingness, mitigate class imbalance and label noise, produce reliable feature importance and calibration, and design an experiment and inference strategy that meets strict latency and resource constraints. It is commonly asked in the Machine Learning domain for Data Scientist roles to assess trade-offs in bias–variance, robustness to correlated features and noise, hyperparameter and encoding decisions, experiment design and evaluation, and tests both conceptual understanding and practical application for production-ready systems.

  Machine LearningTechnical Screen
• AmazonMedium·Data Scientist
  Compare Regularization Techniques and Their Use Cases

  Evaluates model evaluation, regularization, and regression basics for predictive analytics. Strong answers define precision and recall, compare L1, L2, L0, and L-infinity penalties, state OLS assumptions, and contrast linear and logistic regression formulas and losses.

  Machine LearningTechnical Screen
• AmazonMedium·Data Scientist
  Decide standardization, sparse numerics, correlated features

  This question evaluates a candidate's competency in feature engineering and preprocessing for mixed-type tabular data, including handling sparse counts and heavy-tailed monetary features, missingness and zero-inflation, correlated continuous measurements, model-specific scaling needs, and the design of leak-safe pipelines and validation strategies.

  Machine LearningTechnical Screen
• AmazonHard·Data Scientist
  Derive and compare core ML and RL methods

  This question evaluates a candidate's mastery of core machine learning and reinforcement learning concepts, including optimization (gradient methods and batch‑size trade‑offs), supervised versus unsupervised algorithms, policy‑gradient RL and variance reduction, deep RL stability techniques, sequence model complexity (Transformers vs RNNs), embeddings and polysemy, and low‑compute fine‑tuning strategies. It is commonly asked to probe both theoretical understanding and practical engineering judgment about convergence, variance, computational and memory complexity, representation learning, and resource‑constrained model adaptation; the domain is Machine Learning and the assessment spans conceptual understanding and practical application.

  Machine LearningTechnical Screen
• AmazonMedium·Data Scientist
  Design a Churn Model: Handle Missing Data and Justify

  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 a Churn Model: Handle Missing Data and Justify states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• AmazonHard·Data Scientist
  Design a robust traffic forecasting pipeline

  This question tests the ability to design an end-to-end, scalable time-series forecasting pipeline, focusing on anomaly handling, structural decomposition, uncertainty quantification, and model validation strategies.

  Machine LearningTechnical ScreenTime-SeriesForecastingState-Space Models
• AmazonMedium·Data Scientist
  Design an Automated Home-Price Valuation Model

  Design an Automated Home-Price Valuation Model evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• AmazonHard·Data Scientist
  Design an end-to-end spam detection system

  This question evaluates a data scientist's system-design and applied machine learning engineering skills—covering problem framing and labeling, feature representation, model selection and calibration, real-time serving constraints, drift detection, and feedback/safety mechanisms—and is commonly asked to probe trade-offs between latency, precision/recall, and robustness against adversarial evolution in production spam detection. Category: Machine Learning; it tests machine learning systems and production-ML competencies at both conceptual-design and practical-application levels, emphasizing calibration, evaluation (offline and online), operational reliability, and rollback/mitigation planning.

  Machine LearningTechnical Screen
• AmazonEasy·Data Scientist
  Design and evaluate a RAG system

  This question evaluates a candidate's competency in designing and evaluating retrieval-augmented generation (RAG) systems, including document ingestion, chunking, embedding and retrieval strategies, reranking, prompt construction, grounding/citation, operational constraints like latency and freshness, permission handling, and evaluation metrics and failure modes. It is commonly asked to assess practical system-design and applied machine learning skills for LLM applications, testing knowledge in the Machine Learning/Information Retrieval domain and requiring both practical application-level reasoning about trade-offs (latency, cost, precision vs. recall) and conceptual understanding of evaluation and guardrails.

  Machine LearningTechnical Screen
• AmazonHard·Data Scientist
  Evaluate RAG System Accuracy and Cost Control Strategies

  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 Evaluate RAG System Accuracy and Cost Control Strategies states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• AmazonMedium·Data Scientist
  Explain K-Fold Cross-Validation and Its Trade-Offs

  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 Explain K-Fold Cross-Validation and Its Trade-Offs states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• AmazonMedium·Data Scientist
  Explain Overfitting and Underfitting in Machine Learning

  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 Explain Overfitting and Underfitting in Machine Learning states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• AmazonMedium·Data Scientist
  Explain why LASSO selects features

  This question evaluates understanding of regularization and feature selection in linear models, covering competencies in LASSO's L1 penalty versus L2, geometric constraint intuition, optimality/KKT conditions, effects of correlated predictors, the role of standardization, hyperparameter selection, and when Elastic Net is appropriate, within the Machine Learning domain for Data Scientist roles. It is commonly asked because it probes both conceptual understanding and practical application of model sparsity, interpretability, preprocessing, and bias–variance trade-offs, testing knowledge of statistical optimization and model selection rather than implementation details.

  Machine LearningTechnical Screen
• AmazonEasy·Data Scientist
  Implement Batch Gradient Descent for Linear Regression

  Amazon data scientist machine learning prompt on writing batch gradient descent pseudocode for linear regression, including MSE loss, vectorized gradients, parameter updates, stopping criteria, and implementation checks.

  Machine LearningTechnical Screen
• AmazonMedium·Data Scientist
  Optimize Predictive Analytics: Feature Engineering to Model Evaluation

  Evaluates end-to-end predictive analytics project communication for a technical data-science interview. Strong answers cover business objectives, data, leakage controls, features, models, evaluation, errors, and impact.

  Machine LearningTechnical Screen
• AppleMedium·Data Scientist
  Construct a Churn-Prediction Pipeline Using Scikit-Learn

  This question tests practical machine learning engineering skills, specifically the ability to construct an end-to-end classification pipeline for imbalanced tabular data. It evaluates knowledge of preprocessing, model validation, probability calibration, and production packaging — core competencies assessed in data scientist interviews.

  Machine LearningTechnical Screen
• AppleMedium·Data Scientist
  Design Siri-vs-GPT query routing

  This question evaluates a data scientist's competency in designing an end-to-end query-routing system between an on-device personal assistant and a large language model assistant, encompassing objectives, success metrics, ground-truth labeling, feature and model choices, ambiguity and multi-turn handling, and evaluation strategies.

  Machine LearningTechnical Screen
• AtlassianMedium·Data Scientist
  Train and evaluate logistic model with regularization

  This question evaluates proficiency in supervised binary classification with logistic regression, implementation of L1 and L2 regularization, data preprocessing (missingness checks and standardization), hyperparameter selection via cross-validation, and model evaluation using ROC AUC and threshold-dependent metrics.

  Machine LearningTechnical Screen
• Bank of AmericaMedium·Data Scientist
  Explain core probability and ML statistics concepts

  This question evaluates core probability and machine learning statistics competencies including probability basics, descriptive statistics, correlation versus independence, linear regression and regularization, and dimensionality reduction (PCA), falling under the Machine Learning domain for Data Scientist roles and testing both conceptual understanding and practical application. Such multi-part theory questions are commonly asked to probe understanding of fundamental statistical concepts and modeling assumptions, ensuring the candidate can reason about uncertainty, estimator properties, multicollinearity, and eigenstructure without relying solely on implementation details.

  Machine LearningTechnical Screen
• BnpEasy·Data Scientist
  What are linear regression assumptions?

  Assesses understanding of the assumptions underlying ordinary least squares linear regression and the application of diagnostic methods to ensure unbiased parameter estimates and valid statistical inference.

  Machine LearningTechnical Screen
• Boston Consulting GroupMedium·Data Scientist
  Design and sample for credit default prediction

  This question evaluates a data scientist's competencies in imbalanced binary classification, temporal label definition and leakage detection, sampling and calibration under prior-probability shift, metric and operating-point selection, cost-sensitive decision rules, time-based validation and monitoring, and fairness and operational guardrails.

  Machine LearningTechnical Screen
• Boston Consulting GroupHard·Data Scientist
  Reduce overfitting under constraints

  This question evaluates a candidate's competency in machine learning for mitigating overfitting in tabular regression under production latency constraints, testing knowledge of regularization, model complexity control, feature engineering, validation strategies, and experimental design.

  Machine LearningTechnical Screen
• BByteDanceMedium·Data Scientist
  Choose Ranking Functions, Customer Value Metrics, and Predictive Models

  This problem tests the ability to implement SQL window functions for cohort analysis, define robust customer lifetime value metrics, and articulate the trade-offs between various machine learning algorithms in a production setting.

  Machine LearningTechnical ScreenSQLCustomer-AnalyticsModel-Selection
• BByteDanceMedium·Data Scientist
  How would you choose a classification threshold?

  This question evaluates understanding of threshold selection for probabilistic binary classifiers, testing competencies in validation-set decision threshold choice, cost-sensitive trade-offs between false positives and false negatives, effects of class imbalance, and probability calibration.

  Machine LearningTechnical Screen
• BByteDanceHard·Data Scientist
  Predict Short-Video Engagement with Skewed Data

  Design a short-video engagement model with explicit targets, leakage-safe features, heavy-tail handling, exposure awareness, and temporal evaluation.

  Machine LearningTechnical Screen