Data Scientist Interview Questions

Showing 1231–1260 of 1971 results

• MicrosoftHard·Data Scientist
  Design a model for imbalanced conversions

  This question evaluates a data scientist's ability to design and validate end-to-end predictive models for imbalanced binary outcomes, encompassing feature engineering, class imbalance handling, probability calibration, thresholding for budgeted targeting, validation, monitoring, and interpretability.

  Machine LearningTechnical Screen
• MicrosoftMedium·Data Scientist
  Evaluate a Product Change That Adds More User Options

  This question tests the ability to design an A/B test for a feature introducing choice-overload, specifically focusing on metric selection, trade-off analysis, and decision-making frameworks when metrics diverge.

  Analytics & ExperimentationOnsiteExperimentationA/B TestingProduct Metrics
• MicrosoftMedium·Data Scientist
  Explain Deep Learning to a 5-Year-Old Child

  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 Deep Learning to a 5-Year-Old Child states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• MicrosoftEasy·Data Scientist
  Explain KNN and PCA and key tradeoffs

  Evaluates understanding of K-Nearest Neighbors (instance-based classification/regression) and Principal Component Analysis (linear dimensionality reduction), highlighting tradeoffs such as distance metric and preprocessing effects, computational and high-dimensional limitations, PCA’s optimization/computation approaches, and the impact of dimensionality reduction on downstream nonparametric methods. Common in the Machine Learning domain for Data Scientist internships at a fundamentals-to-intermediate abstraction level because it probes both theoretical foundations and practical considerations for applying non-parametric algorithms and linear feature extraction to real datasets.

  Machine LearningTechnical Screen
• MicrosoftEasy·Data Scientist
  Explain KNN and how to tune it

  This question evaluates understanding of the K-Nearest Neighbors algorithm and related competencies such as hyperparameter selection (K, distance metric, weighting), data preprocessing impacts, failure modes in high-dimensional or imbalanced settings, and model evaluation strategies.

  Machine LearningTechnical Screen
• MicrosoftEasy·Data Scientist
  Explain SHAP and build an ML project

  This question tests your conceptual understanding of SHAP values for model interpretability and your ability to design a robust, end-to-end machine learning pipeline for a real-world business problem.

  Machine LearningTechnical ScreenSHAPInterpretabilityMLOps
• MicrosoftMedium·Data Scientist
  Explain SHAP in an ML System

  This question evaluates a data scientist's competence in designing and operationalizing end-to-end machine learning systems in the Machine Learning domain, covering problem framing, label and feature design, model selection, evaluation, deployment, monitoring, and model interpretability using SHAP.

  Machine LearningTechnical Screen
• MicrosoftMedium·Data Scientist
  Find common friends from directed edges

  This question evaluates a candidate's ability to manipulate relational data and reason about graph reciprocity, specifically testing SQL query formulation, aggregation, deduplication, and indexing for performance in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• MicrosoftMedium·Data Scientist
  How would you build and evaluate a classifier?

  This problem tests your ability to design a binary classification pipeline from metric selection to production monitoring, with a focus on aligning technical decisions with business objectives.

  Machine LearningTechnical ScreenClassificationModel EvaluationImbalanced Data
• MicrosoftMedium·Data Scientist
  How would you estimate impact without A/B?

  This question tests your ability to design quasi-experimental methods to infer causal impact when randomization is not possible due to technical or business constraints.

  Analytics & ExperimentationTechnical ScreenCausal InferenceQuasi-experimentsDiD
• MicrosoftHard·Data Scientist
  Implement lower_bound on unknown-size sorted array

  This question evaluates ability to design efficient search algorithms for unknown-length non-decreasing arrays accessed via a limited get(i) API, emphasizing correctness proof and tight query/time complexity bounds.

  Coding & AlgorithmsOnsite
• MicrosoftMedium·Data Scientist
  Implement rotated array binary search with duplicates

  This question evaluates algorithm design and analysis skills, focusing on binary-search variants, handling duplicates in rotated non-decreasing arrays, rotation-index reasoning, and rigorous edge-case analysis; domain: Coding & Algorithms, specifically search algorithms and array manipulation.

  Coding & AlgorithmsOnsite
• MicrosoftMedium·Data Scientist
  Print Org Chart by Level

  This problem tests your ability to model a tree structure from flat parent-child relations and perform a level-order traversal to format hierarchical data.

  Coding & AlgorithmsTechnical ScreenTreesBFSGraphs
• MicrosoftEasy·Data Scientist
  Query departments and top earners

  This problem tests your ability to aggregate data using window functions to identify top-performing rows within specific groups and perform conditional filtering across joined tables.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsJoins
• MicrosoftMedium·Data Scientist
  Test classifier difference with McNemar's test

  This question tests a data scientist's ability to apply McNemar's test for comparing paired classifiers, a core competency in statistical hypothesis testing and model evaluation. It assesses practical knowledge of contingency table analysis, exact vs. asymptotic tests, confidence interval construction, and multiple testing correction in machine learning contexts.

  Statistics & MathOnsite
• MicrosoftEasy·Data Scientist
  Traverse org chart level by level

  This problem tests your ability to reconstruct a tree structure from a flat list of relationships and perform a breadth-first search (BFS) to group employees by their organizational depth.

  Coding & AlgorithmsTechnical ScreenTreesBFSGraphs
• MicrosoftEasy·Data Scientist
  Use confusion matrix to choose model metric

  This question tests the ability to select appropriate classification metrics based on specific business objectives, asymmetric error costs, and dataset characteristics like class imbalance.

  Statistics & MathTechnical ScreenClassificationImbalanced DataEvaluation Metrics
• MicrosoftEasy·Data Scientist
  Write an average-income function

  This problem tests your ability to clean and aggregate numerical data using Python, specifically handling missing values (None/NaN) and calculating a clean arithmetic mean.

  Coding & AlgorithmsTechnical ScreenData CleaningPythonStatistics
• MillenniumMedium·Data Scientist
  Design a Governed Agent for Portfolio Rebalancing

  Design an autonomous, LLM- or ML-driven portfolio rebalancing agent integrated with deterministic guardrails, pre-trade compliance checks, and audit logging. The design must guarantee safety, idempotency, strict separation of concerns, and full state reconstructibility.

  ML System DesignTechnical Screenml-system-designai-agentsrisk-management
• MillenniumMedium·Data Scientist
  Evaluate an Agentic Decision System End to End

  This problem tests the ability to design an automated evaluation framework for an agentic system that transitions from rule-based to LLM-driven decision-making, focusing on decoupling metrics and ensuring safety.

  Software Engineering FundamentalsTechnical ScreenLLM-OpsEvaluationSystems-Design
• MixpanelMedium·Data Scientist
  Calculate First-Week Retention by Signup Cohort

  Compute first-week activity retention by signup-week cohort with all cohort users in the denominator and zero-retention cohorts included.

  Data Manipulation (SQL/Python)Technical Screen
• MixpanelMedium·Data Scientist
  Compare First-Week Retention by Plan Type

  Compare first-week retention by recorded plan type with distinct-user counts, inclusive denominators, and four-decimal rates.

  Data Manipulation (SQL/Python)Technical Screen
• MixpanelMedium·Data Scientist
  Count Users Active After Signup

  Count distinct users with at least one event on or after signup using a PostgreSQL query.

  Data Manipulation (SQL/Python)Technical Screen
• MixpanelMedium·Data Scientist
  Count Users Retained on Day Seven

  Count users active on the seventh calendar day after signup, without double-counting repeated events.

  Data Manipulation (SQL/Python)Technical Screen
• MixpanelMedium·Data Scientist
  Validate Features That Predict User Retention

  Learn to evaluate retention features with leakage-safe time windows, cohort validation, baselines, ablations, and calibrated predictions.

  Machine LearningTechnical Screen
• MolocoMedium·Data Scientist
  Find top advertisers and cost per KPI

  This question evaluates proficiency in data manipulation and analytical SQL/Python skills, including aggregation, ranking with ties, dataset joins at a defined grain, date-based filtering relative to campaign launch dates, and handling division-by-zero scenarios when computing cost metrics.

  Data Manipulation (SQL/Python)Technical Screen
• Morgan StanleyMedium·Data Scientist
  Answer basic probability and statistics questions

  This problem tests your conceptual understanding of probability distributions, statistical bias in data collection, and the relationship between covariance and linear correlation.

  Machine LearningOnline AssessmentStatisticsProbabilityBias
• Morgan StanleyEasy·Data Scientist
  Compute win probability in coin-toss game

  This question evaluates understanding of probability theory and stochastic processes, focusing on win probability in an alternating coin-toss game and reasoning about pattern occurrence and stopping times in random sequences.

  Coding & AlgorithmsTechnical Screen
• Morgan StanleyMedium·Data Scientist
  Describe Your Analysis and Visualization Toolkit

  Tests your ability to articulate an end-to-end data analysis and visualization workflow clearly and concisely for recruiters and hiring managers, connecting technical tool choices directly to business outcomes.

  Data Manipulation (SQL/Python)HR Screendata-analysissqlpython
• Morgan StanleyMedium·Data Scientist
  Estimate Greeks and hedging PnL

  This question evaluates understanding of option Greeks (delta, gamma, vega), mental approximation of Gaussian probabilities, and the ability to reason about delta-hedged profit-and-loss under Black-Scholes model misspecification, categorized under Software Engineering Fundamentals for a Data Scientist role.

  Software Engineering FundamentalsTechnical Screen