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Updated weekly · Last refresh Aug 30

Inc. In Data Scientist Interview Questions

The questions to prepare for a Inc. In Data Scientist interview. Questions from real interview reports rank first. Updated weekly.

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1
A/B Testing & ExperimentationStart here. 3 questions · ~24 min
MDE for Checkout ExperimentMedium

Design a checkout A/B test around MDE selection, power, sample size, guardrails, and a pre-registered ship decision under 14-day traffic limits.

MDEPower AnalysisSample SizeInc. In
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2
Machine Learning9 questions · ~72 min
Extreme Imbalance in Fraud DetectionMedium

Handle rare positive labels in ad fraud detection with the right sampling, loss design, validation, and thresholding strategy.

Feature Engineeringmodel trainingClass ImbalanceInc. In
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3
SQL & Data Manipulation6 questions + 3 drills · ~78 min
Rolling Cohorts with SQL WindowsMedium

Tests ability to use SQL window functions for retention and cohort analytics.

Window FunctionsRetentionCohort AnalysisInc. In
7-Day Rolling Active UsersMedium
Practice
Practice drill

Compute daily active users and a 7-day rolling average using a CTE, distinct counts, and window functions.

Window FunctionsDate FunctionsRunning TotalsMetaSpotifyMeta IT
Window Functions and Top SalariesEasy
Practice
Practice drill

Use ROW_NUMBER and a CTE to return the three highest-paid employees in each Inc. In department.

Window FunctionsInc. In
SQL Duplicate DetectionEasy
Practice
Practice drill

Find duplicate customer profiles by grouping on identifying fields and returning only repeated records.

Group ByHavingAggregationsAdtalem Global EducationAmerisave MortgageCitrine Informatics
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4
Statistics & Probability3 questions · ~24 min
Fraud Rate Significance TestingMedium

Tests statistical reasoning for significance testing and assumption checking.

BiasHypothesis TestingStatistical SignificanceInc. In
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5
Behavioral & Leadership11 questions · ~88 min
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6
More topics8 questions · ~64 min
Evaluate Precision and Recall TradeoffsMedium

Assess precision and recall for a model and explain how the threshold changes the tradeoff.

F1 ScorePrecisionRecallInc. In
Design a Personalized Recommendation RankerHard

Design a personalized recommendation system that turns user preferences into ranked suggestions with retrieval, ranking, and feedback loops.

RetrievalTwo-Tower ModelsRecommendation SystemsInc. In
Define Metrics for New FeaturesMedium

Define a success metric for a new feature that captures real user value, not just raw usage.

MetricsFeature Prioritizationuser valueInc. In
Investigate Sudden Metric DropsMedium

Tests diagnostic analytics and structured troubleshooting for metric anomalies.

Lagging IndicatorsKPIDiagnosisInc. In
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