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Electronic Arts (Ea) Data Scientist Interview Questions

The questions to prepare for a Electronic Arts (Ea) Data Scientist interview. Questions from real interview reports rank first. Updated weekly.

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1
Machine LearningStart here. 11 questions · ~92 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 TradeoffElectronic Arts (Ea)
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2
A/B Testing & Experimentation4 questions · ~34 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 MismatchElectronic Arts (Ea)
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3
Metrics3 questions · ~25 min
Diagnose KPI Drop After ReleaseMedium

Diagnose a post-release KPI drop by separating instrumentation issues from real behavior changes and tracing the problem through the metric hierarchy.

KPILeading IndicatorsDiagnosisElectronic Arts (Ea)
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4
Behavioral & Leadership15 questions · ~126 min
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5
More topics8 questions · ~67 min
SQL Player Ranking by SpendMedium
Practice

Rank EA players by total spend within each region and return the top three using aggregation and ROW_NUMBER().

Window FunctionsRankingGroup ByElectronic Arts (Ea)
Statistical Significance and Confidence IntervalsEasy

Explain how statistical significance and confidence intervals are interpreted in a product experiment.

Confidence IntervalsHypothesis TestingStatistical SignificanceElectronic Arts (Ea)
Optimizing Matchmaking AlgorithmsHard

Tests system-level thinking for improving player experience through data-driven matchmaking changes.

ML RankingModel ServingRecommendation SystemsElectronic Arts (Ea)
Analyzing Player InteractionsMedium

Tests your end-to-end approach to exploratory analysis and actionable insights from player data.

User NeedsUse CasesProduct VisionElectronic Arts (Ea)
Model Evaluation MetricsEasy

Tests your ability to select appropriate metrics based on task type and business or research goals.

PrecisionAccuracyRecallElectronic Arts (Ea)
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Hands-on SQL practiceWrite and run real queries in the editor. 2 drills · ~20 min
SQL Rolling Average and RankingHard
Practice
Practice drill

Use CTEs and window functions to calculate 7-day transaction averages and rank Loft users within each region.

Window FunctionsRankingRunning TotalsLoftArcadisCgi Nederland
Rank Users by Cohort ActivityHard
Practice
Practice drill

Rank Alpaca users within signup cohorts by Q1 activity using LEFT JOIN, aggregation, and RANK().

Window FunctionsRankingGroup ByAlpacaQuantifind
The finish line: interview-readyComplete all 41 questions plus 2 hands-on drills to finish this plan.