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Genius Sports Data Scientist Interview Questions

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

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Behavioral & LeadershipStart here. 9 questions · ~72 min
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More topics10 questions · ~80 min
Pitfalls in Streaming Experiment AnalysisHard

Identify major online experiment pitfalls and explain how they can bias results in a streaming product A/B test.

Network InterferenceNovelty EffectSample Ratio MismatchGenius Sports
Handle Bad Records in Streaming MLMedium

Approach for detecting, isolating, and recovering from missing or corrupted records in a real-time ML pipeline.

Data QualityStream ProcessingmonitoringGenius Sports
Supervised vs Unsupervised for TelemetryMedium

Tests machine learning fundamentals and ability to map methods to sports telemetry use cases.

Unsupervised LearningSupervised LearningGenius Sports
Significance of Accuracy DropsMedium

Tests statistical testing and reasoning for diagnosing live sports data quality issues.

SamplingHypothesis TestingStatistical SignificanceGenius Sports
Real-Time Platform Product MetricsMedium

Tests product thinking and metric design for reliability and engagement in live sports data systems.

MetricsUser NeedsProduct VisionGenius Sports
Schema for Player Movement TrackingMedium

Tests data modeling skills for sports tracking data and event representation.

schema designData ModelingGenius Sports
Diagnose Live Latency Root CauseHard

Tests your incident-style debugging approach using metrics, instrumentation, and data pipeline reasoning.

latencyproject healthDiagnosisGenius Sports
A/B Test for Betting Odds DisplayHard

Tests your experimentation design skills for live betting UX while controlling risk to key outcomes.

experiment designGuardrail Metricsprimary metricsGenius Sports
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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
SQL Data Quality ChecksHard
Practice
Practice drill

Detect missing fields, duplicate transaction keys, invalid amounts, and merchant reference issues with PostgreSQL CTEs and joins.

Data Qualitynull handlingdata integritySigmoid
Identify Funnel Bottlenecks in OnboardingHard
Practice
Practice drill

Use joins, CTEs, and aggregations to find the weakest step in an onboarding funnel and estimate lost conversions.

Funnel AnalysissqlDiagnosisAtlassianRevolut
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 19 questions plus 3 hands-on drills to finish this plan.