Data Scientist Interview Questions

Showing 1201–1230 of 1971 results

• MetaHard·Data Scientist
  Uncover User Needs for Group Calling Effectively

  Meta product analytics prompt on group calling, covering user-needs surveys, competitor benchmarking without direct data, experiment design, network effects, contamination, metrics, and guardrails.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  What features and feature selection would you use?

  This question evaluates feature engineering, feature selection, and production-aware machine learning system design skills for ranking shop ads, within the Machine Learning domain for a Data Scientist role.

  Machine LearningTechnical Screen
• MetaMedium·Data Scientist
  Which clustering algorithm would you use and why

  A Meta Data Scientist machine learning screen on choosing a clustering algorithm for social-product users. It contrasts traditional feature-vector clustering (k-means, GMM, hierarchical, DBSCAN/HDBSCAN) with social-graph community detection (Louvain/Leiden, spectral, SBM, node embeddings), and covers preprocessing, choosing the number of clusters, evaluation, directed/weighted graphs, and scaling to millions of users.

  Machine LearningTechnical Screen
• MetaMedium·Data Scientist
  Write SQL filtering, grouping, CASE, UNION tasks

  This question evaluates proficiency with SQL filtering, aggregation, conditional logic, and set operations—specifically skills around WHERE filtering, GROUP BY/HAVING, CASE bucketing, FLOOR on aggregates, and UNION versus UNION ALL—within the Data Manipulation (SQL/Python) domain and emphasizes practical application of ANSI SQL.

  Data Manipulation (SQL/Python)HR Screen
• MetaMedium·Data Scientist
  Write SQL for CTR and Revenue

  This question tests the ability to join event logs with reference data, perform conditional aggregation to calculate click-through rates (CTR), and apply date-sensitive currency conversions.

  Data Manipulation (SQL/Python)Technical ScreenSQLData AggregationJoin
• MetaMedium·Data Scientist
  Write SQL for Pixel Signal Metrics

  This question evaluates SQL-based data manipulation and analytical competencies, specifically joining and aggregating event and ad performance tables, computing event-weighted metrics and composite data-quality scores (such as validity and match-rate based measures) across a 30-day window.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write SQL for comment analytics

  This question evaluates proficiency in SQL analytics, specifically joins, filtering, aggregation (distinct counts), percentile functions, time-windowed grouping, handling soft-deleted records, and writing efficient queries via CTEs.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write SQL for daily chats and fast replies

  This question evaluates a candidate's ability to manipulate relational data and reason about temporal event sequences, specifically testing skills in SQL aggregation, distinct counting, deduplication, bidirectional relationship handling, and time-windowed event matching.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write SQL for multi-account metrics

  This question tests your ability to perform complex aggregations across multiple tables, specifically using conditional logic and joins to compute user engagement metrics.

  Data Manipulation (SQL/Python)Technical ScreenSQLAggregationJoins
• MetaMedium·Data Scientist
  Write SQL for retention, conversion, and churn

  This question evaluates proficiency in SQL-based analytics and time-aware data manipulation, covering cohort and retention computation, conversion and churn metrics, user-level deduplication across platforms, timezone-local day handling, rolling-window comparisons, and calculation of statistical confidence intervals.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write SQL for seller and category metrics

  This question tests the ability to perform complex aggregations, handle date filtering, and calculate performance ratios while managing potential division-by-zero errors in a multi-table SQL environment.

  Data Manipulation (SQL/Python)Technical ScreenSQLAggregationJoins
• MetaMedium·Data Scientist
  Write SQL for social feed metrics and ties

  This question evaluates advanced SQL analytics skills and competency in data modeling for social feed metrics, including relationship joins, viewer-day temporal aggregation, weighted interaction scoring, and per-user top-N ranking.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write SQL to analyze shop visibility

  This question evaluates proficiency in SQL-based time-series and event-data manipulation, including deduplication of consecutive states, window functions, aggregation, cohort analysis, and percentile/summary statistics.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write SQL to compare exclusive category engagement

  This question evaluates SQL-based data manipulation and cohort analysis competencies, including cohort definition, session-level filtering and aggregation for metrics such as user counts, median active days, proportions, and per-user average minutes while applying data-quality constraints over a fixed date window.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write SQL to flag coordinated fake accounts

  This question evaluates proficiency in SQL-based data manipulation, temporal windowing, event-table joins, aggregation and feature engineering for fraud detection, including computing weighted risk_score flags across user activity.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write SQL to infer group-call demand

  This question evaluates SQL-based data manipulation and product-analytics competencies, including temporal sessionization, graph connectivity inference for detecting overlapping 1:1 call loops, deduplication trade-offs (UNION vs UNION ALL), and computation of derived metrics to estimate latent feature demand within the Data Manipulation (SQL/Python) domain. It is commonly asked because it measures practical application of event-time windowing, connected-component reasoning and edge-case handling (e.g., excluding failed connections) for producing daily summary metrics, emphasizing hands-on SQL proficiency over purely conceptual understanding.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write SQL/pandas for KPI anomaly

  This question evaluates proficiency with SQL window functions and equivalent pandas operations for KPI anomaly detection, covering baseline computation using same-weekday medians over eight weeks, per-country and per-post_type Likes-per-DAU metrics with deduplication of users for DAU, outage filtering, attribution of declines between new and existing users, and time-series comparisons like friend-request acceptance rates while explicitly handling duplicates, sparse or missing DAU rows, and timezone issues. It is commonly asked in Data Manipulation (SQL/Python) interviews to assess practical data engineering and analytical skills, primarily testing practical application with an underlying need for conceptual understanding of baselining, attribution, and data-quality edge cases.

  Data Manipulation (SQL/Python)Onsite
• 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
• MetaMedium·Data Scientist
  Write dating profile report with final reviews

  This question evaluates SQL and data-manipulation proficiency, including temporal joins, versioning, time-bounded snapshotting, windowed aggregations, and validation checks for data quality and idempotency.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  [Analytical Reasoning] Comparing Two Newsfeed Ad Insertion Methods

  Compare independent versus blocked ad insertion in a 100-post feed. Includes expected ads, tail risk, consecutive ad probabilities, longest streaks, product tradeoffs, and A/B test design.

  Analytics & ExperimentationOnsite
• MicrosoftMedium·Data Scientist
  Build and Defend a Baseline Model from a CSV

  Tests end-to-end applied machine learning design on tabular data: framing the prediction target, eliminating data leakage, establishing a leakage-free validation strategy, selecting baselines and evaluation metrics, and planning deployment monitoring and rollback.

  Machine LearningOnsiteModel EvaluationData LeakageMLOps
• 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
• MicrosoftHard·Data Scientist
  Compare CNN/RNN/LSTM and implement K-means

  This question evaluates understanding of deep learning architectures (CNN, RNN, LSTM) and unsupervised clustering implementation (K-means), covering inductive biases, parameter sharing and receptive fields, LSTM gating and vanishing-gradient behavior, tensor shape reasoning, algorithmic complexity, initialization, and cluster handling.

  Machine LearningOnsite
• 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
• MicrosoftMedium·Data Scientist
  Describe leading an ambiguous ML project end-to-end

  This Microsoft Data Scientist onsite behavioral question asks the candidate to narrate an end-to-end machine learning project they led under ambiguity, covering problem framing and success metrics tied to business KPIs, data sourcing and privacy constraints, model selection trade-offs across accuracy, latency, interpretability and cost, offline and online evaluation with a real experiment design, stakeholder alignment with PM, engineering and legal, staged rollout with rollback criteria, and post-launch monitoring. It also probes influence and judgment: a strong PM pushback resolved with data, a disagree-and-commit moment, one honest failure with its root cause, and how reproducibility, fairness and ethics held up under time pressure. Strong answers use STAR-L and quantify every claim, from PR-AUC and p95 latency to cost per thousand predictions and the confidence interval on the business lift.

  Behavioral & LeadershipOnsite
• 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