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Data Scientist Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
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- 546
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- 11
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Showing 421–450 of 641 results
- MetaHardData ScientistResolve teammate feeling unwelcome with measurable steps
This question evaluates leadership competencies in managing psychological safety, conflict resolution, confidentiality, remote-team dynamics, evidence gathering, documentation, escalation, and outcome measurement.
Behavioral & LeadershipOnsite - MetaMediumData ScientistRetrieve Ad Metrics and Rates for Last 7 Days
This question evaluates proficiency in SQL-based data manipulation including joins, conditional aggregation, time-based filtering, and computation of advertising metrics such as impressions, clicks, CTR, and conversion rate.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistRetrieve Top Five Ads by Conversions in 30 Days
This question evaluates a candidate's competency in SQL data manipulation, specifically aggregating event data and applying time-window filters to produce ranked metrics.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistRun a clean A/B test for recommendations
This question evaluates a data scientist's competency in experimental design, causal inference, and statistical power analysis for A/B tests, covering randomization unit choice, contamination prevention, metric and guardrail selection, clustering adjustments, and bias-control techniques.
Analytics & ExperimentationOnsite - MetaMediumData ScientistSize opportunity for new product line
This question evaluates a data scientist's skills in financial opportunity sizing, sensitivity analysis, and experimental design by requiring quantitative modeling of impacted users, incremental orders, revenue, and gross profit for a proposed product line.
Analytics & ExperimentationOnsite - MetaHardData ScientistTest if social users are more engaged
This problem tests your ability to design an analytical framework to measure user engagement, account for selection bias in non-randomized groups, and define robust metrics for comparing heterogeneous user populations.
Analytics & ExperimentationOnsiteAnalyticsCausal InferenceSelection Bias - MetaMediumData ScientistTest two models' proportions for significance
This question evaluates proficiency in statistical inference for proportions, covering hypothesis testing, confidence intervals, power and sample-size calculations, multiple-testing correction, and comparison of frequentist versus Bayesian approaches within the Statistics & Math domain for data scientist roles.
Statistics & MathOnsite - MetaMediumData ScientistTrack Metrics to Measure Push Notification Quality
Track Metrics to Measure Push Notification Quality evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistWrite 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 - MetaMediumData ScientistWrite 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 - MetaMediumData ScientistWrite 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 - MetaMediumData ScientistWrite 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 - MetaMediumData ScientistWrite 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 - MetaMediumData ScientistWrite 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 - MetaMediumData ScientistWrite 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 - MetaMediumData 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 - MicrosoftMediumData ScientistBuild 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 - MicrosoftHardData ScientistCompare 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 - MicrosoftMediumData ScientistDescribe 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 - MicrosoftMediumData ScientistEvaluate 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 - MicrosoftHardData ScientistImplement 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 - MicrosoftMediumData ScientistImplement 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 - MicrosoftMediumData ScientistTest 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 - NetflixMediumData ScientistAddress Fraud Detection with Imbalance and Concept Drift Solutions
Evaluates end-to-end fraud detection ML system design for online payments under drift and imbalance. Strong answers handle delayed labels, class imbalance, feature engineering, cost-sensitive validation, real-time deployment, threshold actions, monitoring, retraining, and concept drift.
Machine LearningOnsite - NetflixMediumData ScientistAggregate D1 retention cohorts in SQL
This question evaluates a candidate's competency in time-series cohort analysis and SQL-based data manipulation, specifically measuring DAU, new-user cohorts, D1 retention and revenue-per-user while handling deduplication and user filtering.
Data Manipulation (SQL/Python)Onsite - NetflixMediumData ScientistAnswer core probability and statistics questions
This question evaluates core probability and statistical inference competencies—covering Bayesian reasoning, regression controls and confounding, the Central Limit Theorem, distribution moments, t-statistics, and effect size/MDE—relevant to quantitative data analysis and hypothesis testing for a Data Scientist role in the Statistics & Math domain.
Statistics & MathOnsite - NetflixMediumData ScientistCompute ITT, TOT, and LATE with noncompliance
This question evaluates understanding of causal inference and experimental analysis, specifically estimation and interpretation of ITT, TOT, and LATE under noncompliance along with classification of compliance types (compliers, always-takers, never-takers, defiers).
Analytics & ExperimentationOnsite - NetflixHardData ScientistDemonstrate handling dismissive stakeholders with candor
This question evaluates a candidate's competency in stakeholder management, candid communication under pressure, conflict de-escalation, requirement elicitation, and outcome-oriented leadership for a Data Scientist role.
Behavioral & LeadershipOnsite - NetflixHardData ScientistDesign a robust conversion propensity model
This question evaluates a candidate's competency in end-to-end propensity modeling, covering correct labeling to avoid post-treatment leakage, time-windowed feature engineering, class-imbalance and calibration strategies, causal uplift estimation, and production concerns such as monitoring, serving consistency, and cold-start scoring.
Machine LearningOnsite - NetflixMediumData ScientistDetermine Maximum Consecutive Order Days Per User
Determine Maximum Consecutive Order Days Per User evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Data Manipulation (SQL/Python)Onsite
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