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Data Scientist Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
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- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 121–150 of 1971 results
- AmazonMediumData ScientistEnsure 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 - AmazonHardData ScientistEstimate Treatment Effects Using PSM, DiD, and DML Methods
Estimate Treatment Effects Using PSM, DiD, and DML Methods 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 & MathOnsite - AmazonHardData ScientistEstimate live sports impact on subscriptions
This question evaluates a data scientist's competence in causal inference and observational study design within analytics and experimentation, covering skills such as defining treatment and control, selecting outcome metrics and covariates, and choosing identification strategies like matching, IV, and difference-in-differences; it is commonly asked to assess the ability to produce credible causal estimates when randomized experiments are unavailable. It tests both conceptual understanding of identification assumptions (e.g., unconfoundedness, overlap, SUTVA, IV validity) and practical application of matching algorithms, propensity modeling, regression specifications, diagnostics, and staggered-rollout/event-study designs for real-world program evaluation.
Analytics & ExperimentationOnsite - AmazonMediumData ScientistEstimate 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 - AmazonEasyData ScientistEvaluate NLP Classification Models
This question evaluates competency in NLP classification model evaluation, covering understanding of confusion matrices, precision/recall/F1/AUC metrics, thresholding and trade-offs, handling class imbalance, and connecting metric choices to operational actions and business costs.
Machine LearningOnsite - AmazonHardData ScientistEvaluate 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 - AmazonMediumData ScientistEvaluate Soft Skills Through Behavioral Interview Questions
Evaluates behavioral soft skills through data-science leadership prompts about helping teammates, diving deep, deadlines, independent decisions, and growth. Strong answers use STAR with concrete actions and outcomes.
Behavioral & LeadershipOnsite - AmazonHardData ScientistEvaluate concession gift-card policy with DID
This question evaluates a data scientist's competency in causal inference and experimentation design, encompassing difference-in-differences with staggered adoption and event-study diagnostics, data inventory and unit-of-analysis specification, clustering choices, heterogeneity and guardrail analyses, and cost–benefit and optional randomized experiment design. Commonly asked in Analytics & Experimentation interviews because it gauges the ability to justify identification strategies, diagnose violations of parallel trends and propose alternatives, and translate empirical results into business-impact metrics, it sits at the intersection of conceptual understanding and practical application within the Data Science domain.
Analytics & ExperimentationOnsite - AmazonMediumData ScientistExplain How You Handled a Missed Commitment
Prepare a behavioral example about a missed deadline, stakeholder communication, recovery, and improved commitment management.
Behavioral & LeadershipTechnical Screen - AmazonMediumData ScientistExplain 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 - AmazonMediumData ScientistExplain 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 - AmazonMediumData ScientistExplain 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 - AmazonMediumData ScientistExplain 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 - AmazonMediumData ScientistExplain 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 - AmazonMediumData ScientistExplain 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 - AmazonMediumData ScientistExplore Dataset to Assess Quality and Choose Visualizations
Evaluates fast dataset exploration, data-quality assessment, and visualization planning for business stakeholders. Strong answers profile schema, check missingness and bias, and choose charts aligned to decisions.
Analytics & ExperimentationOnsite - AmazonMediumData ScientistExplore 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 - AmazonMediumData ScientistFind Top-3 Salaries Per Department Using SQL
This question evaluates SQL data manipulation competency, focusing on group-wise aggregation, ordering, and selecting the top-N salary values within each department.
Data Manipulation (SQL/Python)Onsite - AmazonMediumData ScientistFind 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 - AmazonMediumData ScientistFind top-spend categories per customer with ranking
This question evaluates proficiency with SQL aggregation and JOINs, analytic/window functions (RANK()), tie and NULL/edge-case handling, and percent-of-total calculations for per-customer spend analysis.
Data Manipulation (SQL/Python)Onsite - AmazonMediumData ScientistGenerate 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 - AmazonMediumData ScientistGenerate 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 - AmazonEasyData ScientistHow 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 - AmazonMediumData ScientistHow 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 - AmazonMediumData ScientistHow 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 - AmazonMediumData ScientistIdentify First Daily Order for Each Merchant
This question evaluates proficiency in data manipulation with SQL and related tools, focusing on identifying the first transaction per merchant for each calendar date and handling ordering and deduplication within grouped transactional records.
Data Manipulation (SQL/Python)Onsite - AmazonMediumData ScientistIdentify P-Value Limitations and Complementary Approaches
Identify P-Value Limitations and Complementary Approaches 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 & MathOnsite - AmazonEasyData ScientistImplement 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 - AmazonMediumData ScientistImplement 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 - AmazonMediumData ScientistImplement Reservoir Sampling; Analyze Time and Space Complexity
This question evaluates understanding of streaming randomized algorithms and probabilistic reasoning—specifically reservoir sampling—along with analysis of time and space complexity and use of hash-based structures for duplicate detection; it is in the Coding & Algorithms domain and tests both conceptual probability invariants and practical implementation skills. Such problems are commonly asked to gauge a candidate's ability to design one-pass, linear-time solutions for unbounded data streams and to reason about algorithmic correctness and resource bounds in technical interviews.
Coding & AlgorithmsOnsite
Amazon