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Updated weekly · Last refresh Oct 17

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 daily.

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~5htotal time
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1
A/B Testing & ExperimentationStart here. 3 questions · ~25 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 Learning8 questions · ~66 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 Manipulation7 questions + 2 drills · ~78 min
Simulate Terminal cd CommandsMedium

Evaluates algorithmic thinking for parsing commands and maintaining correct directory state.

Inc. 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
SQL Extraction With JoinsHard
Practice
Practice drill

Extract filtered monthly category revenue and prior-period changes using joins, aggregations, and LAG.

JoinssqlAggregationsInc.VisaInc. In
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4
Statistics & Probability3 questions · ~25 min
Linear Regression with P-ValuesMedium

Evaluates statistical foundations for interpreting regression results.

linear regressionInc. In
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5
Behavioral & Leadership10 questions · ~83 min
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6
More topics8 questions · ~66 min
Design Real-Time Fraud Risk ScoringHard

Design a real-time fraud scoring system for card transactions with strict latency, delayed labels, and high availability requirements.

Feature StoreFeature DriftModel ServingInc. In
Evaluate Precision and Recall TradeoffsMedium

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

F1 ScorePrecisionRecallInc. 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
DS Pipeline ExpectationsMedium

Assesses practical pipeline understanding across SQL and Python for DS workflows.

Inc. In
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The finish line: interview-readyComplete all 39 questions plus 2 hands-on drills to finish this plan.