Data Scientist Interview Questions

Showing 61–90 of 1971 results

• 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
• AmazonHard·Data Scientist
  Compare Tableau live vs extract and filters

  This question evaluates competency in BI data architecture and large-scale dashboard performance, covering extract vs live connections, refresh and incremental strategies, query pushdown versus in-tool processing, row-level security, filter order-of-operations, performance tuning, and platform migration mapping within the Analytics & Experimentation domain for a Data Scientist role. It is commonly asked to assess trade-off reasoning across data freshness, storage, query performance and security for a 100M-row fact table, combining practical application-level decisions (implementation and tuning) with conceptual understanding (filter ordering, calculations, and migration pitfalls) when moving between BI platforms.

  Analytics & ExperimentationOnsite
• AmazonMedium·Data Scientist
  Compute a Rolling Seven-Day Revenue Sum with Missing Dates

  Tests the ability to write a PostgreSQL query that generates a continuous daily time series per seller, handles missing sales dates, filters by order status, and computes a rolling 7-day revenue sum.

  Data Manipulation (SQL/Python)Technical Screensqlpostgresqlwindow-functions
• AmazonMedium·Data Scientist
  Compute an A/B test p-value by hand

  This question evaluates understanding of hypothesis testing for proportions, specifically the two-proportion z-test, p-value computation and interpretation of statistical significance in an A/B test with binary outcomes.

  Statistics & MathTechnical Screen
• AmazonMedium·Data Scientist
  Compute and interpret quantile loss vs RMSE

  This question tests the ability to distinguish between point-prediction error metrics and quantile loss, and the practical implications of choosing an evaluation metric in the context of asymmetric business costs.

  Statistics & MathTechnical ScreenStatisticsForecastingLoss Functions
• AmazonMedium·Data Scientist
  Compute array modes with ties and no-mode rule

  This question evaluates algorithm design and data-structure skills—frequency counting, hashing, and handling edge cases such as ties and a no-mode rule—within the coding and algorithms domain for data scientist roles.

  Coding & AlgorithmsTechnical Screen
• AmazonMedium·Data Scientist
  Compute daily work hours from in/out events

  This question evaluates a candidate's ability to manipulate time-series punch-event data, including temporal pairing of in/out events, splitting shifts across calendar days, rounding and aggregating hours, and detecting data-quality issues such as overlaps or orphaned events using SQL or Python.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Data Scientist
  Compute join counts and window ranks

  This question evaluates SQL data-manipulation and analytic competencies including join semantics, set operations, window functions, view versus materialized view trade-offs, key and partition design, and query-tuning techniques in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Data Scientist
  Compute p-values, CIs, and adjust multiples

  This question evaluates a candidate's proficiency in statistical inference, covering hypothesis testing and test selection, confidence interval construction, multiple-testing corrections, error types, and asymptotic reasoning.

  Statistics & MathTechnical Screen
• AmazonMedium·Data Scientist
  Consolidate and Rank Global Salaries in USD

  This question evaluates data consolidation, currency normalization, and ranking competencies, measuring the ability to combine country-specific employee data and convert salaries into a common USD basis within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Create Country-Level Spend Report Using Pandas

  This question evaluates proficiency in data manipulation using pandas, including relational joins and aggregation to produce country-level summary statistics like totals and averages.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Create SQL Queries for Sales and Customer Analysis

  This question evaluates proficiency in SQL-based data manipulation, covering aggregation and dispersion metrics, null handling, relational joins, and window functions for ranking customers.

  Data Manipulation (SQL/Python)Technical 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
• AmazonMedium·Data Scientist
  Deliver a Data Solution Under Tight Deadlines

  Deliver a Data Solution Under Tight Deadlines evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipTechnical Screen
• AmazonMedium·Data Scientist
  Demonstrate Amazon LP with deep follow-ups

  This question evaluates behavioral and leadership competencies for a Data Scientist role, focusing on customer orientation, ownership, accountability for missed deadlines, and responsiveness to negative feedback while requiring quantified impact, scope, dates, and trade-offs.

  Behavioral & LeadershipOnsite
• AmazonMedium·Data Scientist
  Demonstrate Initiative Beyond Job Responsibilities

  Evaluates ownership and curiosity beyond formal job responsibilities. Strong answers show a concrete initiative, explain what the candidate had to learn, describe stakeholder alignment and de-risking, quantify the impact, and reflect on how the experience changed their approach.

  Behavioral & LeadershipTechnical Screen
• AmazonMedium·Data Scientist
  Demonstrate Leadership and Ownership in Energy Analytics Role

  Amazon-style data scientist behavioral prompt for an energy analytics role, covering Ownership, STAR storytelling, resume walkthrough, transferable analytics skills, quantified impact, and motivation for the domain.

  Behavioral & LeadershipOnsite
• AmazonHard·Data Scientist
  Demonstrate Leadership in Challenging Situations and Decision-Making

  Demonstrate Leadership in Challenging Situations and Decision-Making evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipOnsite
• AmazonMedium·Data Scientist
  Demonstrate calculated risk and deep-dive leadership

  This question evaluates calculated risk-taking, initiative, deep-dive leadership, experimental design, risk management, and outcome quantification skills relevant to a Data Scientist role.

  Behavioral & LeadershipTechnical Screen
• AmazonMedium·Data Scientist
  Demonstrate invent-and-simplify and customer communication

  This question evaluates a candidate's competency in invent-and-simplify and difficult customer communication, emphasizing process redesign, trade-off analysis, risk mitigation, stakeholder persuasion, measurable outcomes, and clear written alignment.

  Behavioral & LeadershipTechnical Screen
• AmazonHard·Data Scientist
  Demonstrate leadership in data-driven scenarios

  This question evaluates leadership and behavioral competencies alongside core data-science skills such as metric design, statistical validation, experimentation, root-cause analysis, stakeholder management, and cross-functional ownership within a Behavioral & Leadership and Data Science domain.

  Behavioral & LeadershipOnsite
• AmazonMedium·Data Scientist
  Demonstrate leadership under disagreement

  This question evaluates leadership competencies in stakeholder management, influence without authority, quantitative risk communication, and decision-making under deadline within a Behavioral & Leadership context for a Data Scientist role.

  Behavioral & LeadershipOnsite
• AmazonMedium·Data Scientist
  Demonstrate leadership with quantifiable STAR stories

  This question evaluates leadership, ownership, and communication competencies for a Data Scientist by requiring concise, quantifiable STAR(L) narratives that capture role, team, stakeholders, constraints, actions, trade-offs, and measurable results.

  Behavioral & LeadershipTechnical Screen
• AmazonMedium·Data Scientist
  Demonstrate ownership beyond responsibilities

  This question evaluates ownership, proactive initiative, cross-functional leadership, stakeholder alignment, risk management, handling resistance, trade-off analysis, and the ability to quantify business impact within a Data Scientist role.

  Behavioral & LeadershipTechnical Screen
• AmazonMedium·Data Scientist
  Demonstrate problem-solving under resistance

  This question evaluates a data scientist's end-to-end problem-solving, cross-functional leadership, stakeholder management, and ability to drive measurable business impact under organizational resistance, covering experimental design, data validation, risk assessment, trade-off decisions, and adoption/verification metrics.

  Behavioral & LeadershipOnsite
• 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
• AmazonHard·Data Scientist
  Describe Managing an End-to-End Project for Scalability

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Managing an End-to-End Project for Scalability states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipTechnical Screen