Data Scientist Interview Questions

Showing 61–90 of 1076 results

• 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
• AmazonMedium·Data Scientist
  Determine Discount's Effect on Conversion Rate with A/B Testing

  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 Determine Discount's Effect on Conversion Rate with A/B Testing states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• AmazonMedium·Data Scientist
  Determine Length of Longest Unique Substring

  This question evaluates string-processing skills and algorithmic problem-solving, including handling character uniqueness and performance considerations. Commonly asked in Coding & Algorithms interviews to assess a candidate's ability to implement efficient solutions and reason about time/space trade-offs, it targets practical application rather than purely conceptual understanding.

  Coding & AlgorithmsTechnical Screen
• AmazonMedium·Data Scientist
  Determine Probability of Both Children Being Boys

  Evaluates conditional probability reasoning in the classic two-children problem with different observation mechanisms. Strong answers enumerate ordered outcomes, condition correctly, and compare at-least-one, older-child, and random-child cases.

  Statistics & MathTechnical Screen
• AmazonMedium·Data Scientist
  Diagnose Causes and Test Hypotheses for Metric Drop

  Evaluates incident-style analysis of a sudden purchase-conversion drop in a consumer product. Strong answers list plausible causes, validate with concrete cuts and logs, design an A/B test, and define metrics.

  Analytics & ExperimentationTechnical Screen
• AmazonMedium·Data Scientist
  Ensure Data Quality and Deliver Impact Amid Challenges

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Ensure Data Quality and Deliver Impact Amid Challenges states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipTechnical Screen
• AmazonMedium·Data Scientist
  Estimate shuttle impact with robust causal design

  This question evaluates a candidate's competency in causal inference and experimental analytics, covering staggered-adoption difference-in-differences design, estimand and outcome definition, parallel-trends diagnostics, selection and time-varying confounding considerations, clustering and weighting choices, handling varied adoption timing and site attrition, robustness checks, and communication of coefficient interpretation and uncertainty. It is commonly asked in the Analytics & Experimentation domain because it tests both conceptual understanding of identification assumptions and practical application of statistical design choices for real-world observational causal analysis.

  Analytics & ExperimentationTechnical 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 How You Handled a Missed Commitment

  Prepare a behavioral example about a missed deadline, stakeholder communication, recovery, and improved commitment management.

  Behavioral & LeadershipTechnical 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 P-value, Confidence Interval, and Multiple Testing Adjustments

  Evaluates A/B testing inference fundamentals, including p-values, confidence intervals, multiple testing adjustments, Type I and Type II errors, z-tests versus t-tests, and CLT versus LLN. Strong answers connect definitions to experiment decisions and common pitfalls.

  Statistics & MathTechnical Screen
• AmazonMedium·Data Scientist
  Explain Propensity Score Matching and Assess Covariate Balance

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Explain Propensity Score Matching and Assess Covariate Balance states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• AmazonMedium·Data Scientist
  Explain Statistical Outputs to Non-Technical Stakeholders

  Explain Statistical Outputs to Non-Technical Stakeholders evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical 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
• AmazonMedium·Data Scientist
  Explore Subscription Patterns and Status Transitions with SQL/Pandas

  This question evaluates proficiency in time-series and event-sequence data manipulation, temporal aggregation, and data quality validation using SQL and pandas, focusing on identifying status transitions and date-based uniqueness.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Find recommended friend pairs by shared songs

  This question evaluates data manipulation skills—specifically SQL joins and anti-joins, deduplication, date/time handling, aggregation, and pairwise overlap computation—within the Data Manipulation (SQL/Python) domain at a practical implementation level requiring query-writing to produce per-day overlap counts.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Generate Synthetic Clickstream Data with Python Function

  This question evaluates a candidate's ability to implement synthetic data generation and probabilistic event simulation in Python using libraries like pandas and numpy, emphasizing timestamp handling, random sampling, and feature engineering for clickstream records.

  Coding & AlgorithmsTechnical Screen
• AmazonMedium·Data Scientist
  Generate primes up to n efficiently

  This question evaluates algorithm design and complexity analysis for prime generation, focusing on sieving techniques and time-space trade-offs when producing primes up to large n.

  Coding & AlgorithmsTechnical Screen
• AmazonEasy·Data Scientist
  How to evaluate adding video ads in a game

  This question tests your ability to design an experimentation framework and evaluate the business impact of introducing interstitial video ads in a mobile game, focusing on the trade-off between short-term revenue and long-term user retention.

  Analytics & ExperimentationTechnical ScreenA/B TestingProduct AnalyticsMonetization
• AmazonMedium·Data Scientist
  How would you evaluate adding video ads?

  This question evaluates a data scientist's competencies in experimental design, causal inference, metrics selection, and product monetization analytics within the Analytics & Experimentation domain, focusing on measuring the impact of video ads on revenue, engagement, retention, and lifetime value.

  Analytics & ExperimentationTechnical Screen
• AmazonMedium·Data Scientist
  How would you test a price increase?

  An Amazon data scientist technical screen case on pricing: decide whether to raise the price of a subscription product, choose the price point and packaging, and design the experiment that proves it. The answer covers per-visitor profit and LTV metrics, guardrails, elasticity estimation, randomization on the billing account, and how to handle existing subscribers at renewal. It also fixes the two traps that sink most answers: judging a subscription price test on short-horizon revenue, and measuring churn only among users who converted.

  Analytics & ExperimentationTechnical 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
  Implement Function to Determine Mode and Prime Numbers

  This question evaluates algorithmic problem-solving skills, including frequency analysis for mode computation, correct handling of ties and uniqueness edge cases, and basic number-theoretic competency for prime identification.

  Coding & AlgorithmsTechnical Screen
• AmazonMedium·Data Scientist
  Implement a high-throughput web crawler safely

  This question evaluates competency in concurrent systems, scalable data ingestion, and algorithmic design for a high-throughput web crawler, covering URL normalization, deduplication, per-host rate limiting, fault-tolerant checkpointing, and bounded-memory queueing.

  Coding & AlgorithmsTechnical Screen
• AmazonMedium·Data Scientist
  List Top Customers and Monthly Order Counts in SQL

  This question evaluates a candidate's ability to perform SQL aggregation, grouping, top‑N selection, and date-based summarization to compute customer spend and chronological monthly order counts.

  Data Manipulation (SQL/Python)Technical Screen