Data Scientist Interview Questions

Showing 1–30 of 163 results

• Akuna CapitalHard·Data Scientist
  Find minimum swaps to sort array with duplicates

  This question evaluates array manipulation and permutation-based reasoning for minimizing swap operations, including handling of duplicate values and performance considerations, within the Coding & Algorithms domain for a Data Scientist role.

  Coding & AlgorithmsOnline Assessment
• AmazonMedium·Data Scientist
  Diagnose MySQL joins and GROUP BY/HAVING errors

  This question evaluates mastery of SQL join semantics (LEFT, RIGHT, FULL OUTER emulation, CROSS), NULL equality and duplicate handling, GROUP BY/HAVING behavior under ONLY_FULL_GROUP_BY, aggregation result shaping and type/format conversions in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Online Assessment
• AmazonHard·Data Scientist
  Drive stakeholder alignment under trade-offs

  This question evaluates competency in cross-functional stakeholder alignment, trade-off analysis, data governance and security risk assessment, executive communication, and program decision-making within a technical product context.

  Behavioral & LeadershipOnline Assessment
• AmazonMedium·Data Scientist
  Prioritize Tasks and Respond to Coworker Concerns

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Prioritize Tasks and Respond to Coworker Concerns states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipOnline Assessment
• AmazonHard·Data Scientist
  Quantify build-vs-buy training decision

  This question evaluates proficiency in quantitative decision analysis, causal inference for experimentation, KPI definition, and risk‑adjusted financial modeling within the Analytics & Experimentation domain, assessing skills relevant for a Data Scientist making operational trade‑offs.

  Analytics & ExperimentationOnline Assessment
• AmazonHard·Data Scientist
  Solve subset-count and kth-factor problems

  This question evaluates algorithm design and analysis skills, including selection under sum constraints with negatives and zeros, streaming and query-preprocessing strategies for repeated budgets, and number-theoretic factor enumeration for large N, all within the Coding & Algorithms domain.

  Coding & AlgorithmsOnline Assessment
• AqrMedium·Data Scientist
  Autopilot a Thruster Drone Through Randomized Canyon Walls

  Write a per-tick controller that fires a drone's thruster so it clears wall openings on unseen, randomized canyons. Tests modeling deterministic physics, depth-limited lookahead search, and choosing a horizon that generalizes.

  Software Engineering FundamentalsOnline Assessmentsimulationsearchgame-ai
• AqrMedium·Data Scientist
  Autopilot for a Flappy Bird-Style Game Across Maps with Varying Pipe Gaps

  Build an autopilot for a Flappy Bird-style game that decides on every step whether the bird should flap so it flies through pipe gaps whose heights vary across unseen maps. Tests modeling the game's physics, planning ahead under a time budget, collision handling, and generalizing beyond a single map.

  Software Engineering FundamentalsOnline Assessment
• AqrMedium·Data Scientist
  Autopilot for a Submarine Navigating Shifting Reef Gaps

  Write a controller that decides, on every tick, whether a mini-submarine should blow ballast to rise through reef-pair gaps of varying heights on unseen maps, using a short deterministic lookahead search.

  Software Engineering FundamentalsOnline Assessmentsimulationsearchgame-ai
• AtlassianMedium·Data Scientist
  Implement sliding-window rate limiter with dual thresholds

  This question evaluates a candidate's skill in implementing efficient online sliding-window rate limiting, covering stream processing, time-based window management, per-key state maintenance, algorithmic complexity analysis, and formal correctness reasoning.

  Coding & AlgorithmsOnline Assessment
• AtlassianMedium·Data Scientist
  Minimize max L1 radius with k centers in 1D

  This question evaluates algorithmic problem-solving skills and understanding of clustering and optimization concepts, specifically the 1D k-center (minimax L1) problem, greedy feasibility tests, binary-search-on-answer techniques, numerical stability with large coordinates, and time/space complexity analysis relevant for a Data Scientist role.

  Machine LearningOnline Assessment
• Bank of AmericaMedium·Data Scientist
  Answer behavioral prompts for quant internship

  Answer behavioral prompts for quant internship 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 & LeadershipOnline Assessment
• Bank of AmericaMedium·Data Scientist
  Compute maximum beauty of a string

  This question evaluates skills in string manipulation, frequency analysis, and optimization of weighted sums, focusing on assigning distinct values to characters to maximize an aggregate metric.

  Coding & AlgorithmsOnline Assessment
• Bank of AmericaMedium·Data Scientist
  Convert number words to integer

  This question evaluates textual number parsing, tokenization, and numeric mapping skills, testing string manipulation and lexical interpretation within the Coding & Algorithms domain for a Data Scientist role and emphasizing practical application rather than pure theoretical concepts.

  Coding & AlgorithmsOnline Assessment
• Bank of AmericaMedium·Data Scientist
  Describe building a professional relationship

  This question evaluates interpersonal communication, collaboration, stakeholder management, and leadership competencies through demonstrated relationship-building.

  Behavioral & LeadershipOnline Assessment
• Bank of AmericaMedium·Data Scientist
  Explain coding solution and alternatives

  Explain coding solution and alternatives evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Coding & AlgorithmsOnline Assessment
• Bank of AmericaMedium·Data Scientist
  Showcase initiative and collaboration examples

  This prompt evaluates initiative, ownership, quantitative rigor, complex problem-solving, and relationship-building within a data scientist role. Commonly asked to gauge leadership, cross-functional impact, and measurable technical outcomes, it is categorized as Behavioral & Leadership in the data science domain and emphasizes practical application of analytical methods alongside conceptual reasoning about trade-offs and collaboration.

  Behavioral & LeadershipOnline Assessment
• BBarclaysEasy·Data Scientist
  Determine Worker Counts from Production Ratios

  This problem tests mathematical modeling and systems of linear equations by requiring the candidate to derive specific headcount requirements based on production demand and individual productivity constraints.

  Statistics & MathOnline AssessmentAlgebraSystems of EquationsOptimization
• Boston Consulting GroupMedium·Data Scientist
  Achieve 0.95 precision via thresholding

  This question evaluates probabilistic classifier calibration, threshold selection to achieve a target precision, handling severe class imbalance, and precision–recall based evaluation metrics.

  Machine LearningOnline Assessment
• Boston Consulting GroupMedium·Data Scientist
  Build a leak-free sklearn pipeline

  This problem tests the ability to build a robust scikit-learn pipeline that prevents data leakage during preprocessing and hyperparameter tuning while handling class imbalance.

  Machine LearningOnline Assessmentscikit-learndata-leakagepipelines
• Boston Consulting GroupMedium·Data Scientist
  Build and evaluate imbalanced binary classifier

  This question evaluates a data scientist's skills in building reproducible machine learning pipelines for imbalanced binary classification, covering temporal splitting to avoid leakage, class imbalance handling, feature preprocessing, probability calibration, threshold selection, and monitoring for calibration drift.

  Machine LearningOnline Assessment
• Boston Consulting GroupEasy·Data Scientist
  Calculate Probability and Statistics for Dice Roll Outcomes

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Probability and Statistics for Dice Roll Outcomes states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnline Assessment
• Boston Consulting GroupEasy·Data Scientist
  Calculate Probability of Heads and Red Balls Drawing

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Probability of Heads and Red Balls Drawing states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnline Assessment
• Boston Consulting GroupMedium·Data Scientist
  Compute posterior and predictive coin probabilities

  This question evaluates Bayesian inference and probabilistic reasoning, focusing on posterior updating, posterior predictive probability, and the conditional expectation of a stopping time given observed outcomes.

  Statistics & MathOnline Assessment
• Boston Consulting GroupHard·Data Scientist
  Detect Data Leakage in Supervised Learning Pipelines

  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 Detect Data Leakage in Supervised Learning Pipelines states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnline Assessment
• Boston Consulting GroupMedium·Data Scientist
  Improve Model Generalization with Cross-Validation and Feature Engineering

  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 Improve Model Generalization with Cross-Validation and Feature Engineering states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnline Assessment
• Boston Consulting GroupMedium·Data Scientist
  Merge and Clean Customer Order Data for Analysis

  This question evaluates proficiency in data manipulation with pandas and SQL concepts, focusing on dataset merging, missing-value handling, and computing derived aggregate columns such as total_spent.

  Data Manipulation (SQL/Python)Online Assessment
• Boston Consulting GroupMedium·Data Scientist
  Merge and Concatenate Inconsistent Order Files with Pandas

  This question evaluates proficiency with pandas-based data manipulation, including schema alignment, column renaming, type casting, and vertical concatenation when merging CSV files.

  Data Manipulation (SQL/Python)Online Assessment
• Boston Consulting GroupEasy·Data Scientist
  Scale and Normalize: When to Use Each Method?

  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 Scale and Normalize: When to Use Each Method? states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnline Assessment
• Boston Consulting GroupEasy·Data Scientist
  Train GradientBoostingClassifier with 5-Fold Cross-Validation

  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 Train GradientBoostingClassifier with 5-Fold Cross-Validation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnline Assessment