Data Scientist Interview Questions

Showing 661–690 of 1076 results

• MetaMedium·Data Scientist
  Write advanced SQL for sales support analytics

  This question evaluates practical SQL data-manipulation skills such as joins, aggregation, window functions, time-based filtering, deduplication, and computation of sales KPIs for rep-level analytics.

  Data Manipulation (SQL/Python)Technical Screen
• MicrosoftMedium·Data Scientist
  Choose Classification Metrics Under Asymmetric Costs

  This question evaluates understanding of binary classification evaluation metrics and model-operating decisions, covering confusion-matrix-derived measures (precision, recall/sensitivity, specificity, false positive/negative rates), summary metrics (ROC-AUC, PR-AUC), Type I/II errors, threshold selection, prevalence drift, probability calibration, and cost-sensitive trade-offs. It is commonly asked to assess a data scientist's ability to reason about model performance under class imbalance and asymmetric business costs, and it belongs to the Statistics & Math / Machine Learning model-evaluation domain requiring both conceptual understanding and practical application.

  Statistics & MathTechnical Screen
• MicrosoftEasy·Data Scientist
  Compute P(Bag B | red) via Bayes

  This question evaluates understanding of Bayes' rule and conditional probability by requiring computation of the posterior probability that a particular bag was chosen given a red draw.

  Statistics & MathTechnical Screen
• MicrosoftEasy·Data Scientist
  Compute and plot a precision–recall curve

  This question evaluates understanding of binary classification evaluation metrics—precision, recall, precision–recall curves, and related summaries like Average Precision/AUPRC—within the Machine Learning domain and is relevant for Data Scientist roles.

  Machine LearningTechnical Screen
• MicrosoftMedium·Data Scientist
  Describe Overcoming Challenges in Machine Learning Projects

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Overcoming Challenges in Machine Learning Projects states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipTechnical Screen
• MicrosoftEasy·Data Scientist
  Describe resolving a conflict with a teammate

  This Behavioral & Leadership interview question evaluates conflict resolution, interpersonal communication, ownership, and technical decision‑making skills within a data science and ML research project context.

  Behavioral & LeadershipTechnical Screen
• MicrosoftMedium·Data Scientist
  Design Product-Description Content Moderation at Marketplace Scale

  Design an ML moderation system for multilingual seller descriptions at marketplace scale. Cover policy-aware labels, severity-based actions, calibrated thresholds, human review and appeals, low-latency serving, selective rescans, auditability, drift, and adversarial abuse.

  ML System DesignTechnical Screen
• MicrosoftMedium·Data Scientist
  Design Testing Without A/B Experiments

  This question evaluates a data scientist's mastery of causal inference, observational study methods, metric definition, experimental design trade-offs, and failure-mode analysis when randomized A/B testing is infeasible.

  Analytics & ExperimentationTechnical Screen
• 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
  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
• 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
• 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