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Updated weekly · Last refresh Sep 24

OfferUp Data Scientist Interview Questions

The questions to prepare for a OfferUp Data Scientist interview. Questions from real interview reports rank first. Updated daily.

30questions
~4htotal time
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1
Machine LearningStart here. 10 questions · ~81 min
Handling Imbalanced Fraud LabelsMedium

Explain how to train and evaluate models on highly imbalanced fraud data without relying on misleading accuracy.

Cross-ValidationFeature EngineeringSupervised LearningOfferUp
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2
Behavioral & Leadership9 questions · ~73 min
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3
More topics11 questions · ~89 min
Design Test for New FeatureMedium

Design an A/B test for a new platform feature, including success metrics, power, guardrails, and a clear ship decision.

experiment designfeature evaluationA/B TestingOfferUp
Statistical Significance in Hypothesis TestingEasy

Explain what statistical significance means and why it matters when interpreting experimental or analytical results.

Hypothesis TestingData AnalysisStatistical SignificanceOfferUp
First Checks for Metric DropsEasy

Outline the first checks to diagnose a sudden drop in a core product metric, starting with data quality, scope, and decomposition.

Lagging IndicatorsLeading IndicatorsDiagnosisOfferUp
Spam Listing Suppression SystemHard

Tests system design skills for marketplace trust and safety using data-driven detection.

system designquality controlOfferUp
Investigate Metric Drop with LogsMedium

Assesses debugging and causal investigation skills using instrumentation and log data.

root cause analysisOfferUp
Recommendation System EvaluationMedium

Tests your knowledge of offline and online metrics for recommender systems.

performance metricsRecommendation SystemsOfferUp
SQL Window FunctionsMedium

Tests practical SQL skills for analytics tasks using windowed computations.

Window FunctionsData AnalysissqlOfferUp
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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
SQL Window Functions Moving AverageMedium
Practice
Practice drill

Calculate a three-day moving average of active-client revenue using aggregation and SQL window functions.

Window FunctionsData AnalysissqlAAreteEEvalueserveSShyena Tech Yarns
Running Average With Window FunctionsEasy
Practice
Practice drill

Calculate each Hinge user's 30-day rolling average of daily interactions using CTEs and window functions.

Window FunctionsData AnalysissqlHingeCCatalyst Operations & AnalyticsAAlight Solutions
Using SQL Window FunctionsMedium
Practice
Practice drill

Calculate three-day rolling passenger entry averages for TfL stations using aggregation and window functions.

Window FunctionsData AnalysissqlTransport for LondonThe Trade DeskMercari
The finish line: interview-readyComplete all 30 questions plus 3 hands-on drills to finish this plan.