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Updated weekly · Last refresh Aug 30

Oak Street Health Data Scientist Interview Questions

The questions to prepare for a Oak Street Health Data Scientist interview. Questions from real interview reports rank first. Updated weekly.

27questions
~4htotal time
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1
Machine LearningStart here. 8 questions · ~64 min
Supervised vs Unsupervised LearningEasy

Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example.

Unsupervised LearningFeature EngineeringBias-Variance TradeoffOak Street Health
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2
Pipelines4 questions · ~32 min
Database Architecture Trade-offsHard

Tests ability to reason about data storage, performance, and operational constraints for healthcare datasets.

InfrastructureData ModelingQualityOak Street Health
Real-Time Patient Monitoring PipelineHard

Tests your ability to build low-latency pipelines and support timely decision-making in care.

InfrastructureStream ProcessingOrchestrationOak Street Health
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3
Statistics & Probability8 questions · ~64 min
Computing CorrelationEasy

Tests ability to calculate and interpret correlation for exploratory analysis.

RegressionCorrelationData WranglingOak Street Health
Analyzing Patient Outcomes for ImprovementsHard

Tests ability to analyze outcomes data and translate results into actionable improvement recommendations.

ExperimentationRegressionCausal InferenceOak Street Health
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4
Product Sense3 questions · ~24 min
Motivation for Healthcare Data ScienceEasy

Tests motivation and fit for healthcare-focused data science aligned to prevention and outcomes.

User NeedsValue PropositionProduct VisionOak Street Health
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5
More topics4 questions · ~32 min
Handling Missing DataHard

Tests data quality handling and correct treatment of missingness.

Window FunctionsData WranglingCTEsOak Street Health
Identifying Key MetricsMedium

Tests ability to define metrics aligned to business goals and measurable outcomes.

KPIsLeading IndicatorsDiagnosisOak Street Health
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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
Monthly Sales Trends by CategoryMedium
Practice
Practice drill

Aggregate monthly sales by product category and use LAG to calculate month-over-month changes.

InfrastructureToolsData WranglingTotal Wine & MoreInc.Benjamin Moore
Handle Missing Financial Input ValuesMedium
Practice
Practice drill

Use joins, CTEs, and CASE logic to flag and fill missing financial fields for Qlik planning records.

Data WranglingETLQualityQlik
Contact Center Agent Performance MetricsHard
Practice
Practice drill

Compute daily agent call KPIs and SLA using joins, aggregations, and window ranking in a contact center model.

ETLAggregationsData ModelingADP
The finish line: interview-readyComplete all 27 questions plus 3 hands-on drills to finish this plan.