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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 daily.

50questions
~7htotal time
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1
A/B Testing & ExperimentationStart here. 8 questions · ~67 min
Evaluate a New Game MechanicHard

Assess whether a new game mechanic improves engagement without harming retention or monetization.

ExperimentationGuardrail MetricsA/B TestingElectronic Arts (Ea)
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2
Statistics & Probability7 questions · ~58 min
Statistical Significance and Confidence IntervalsEasy

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

Confidence IntervalsHypothesis TestingStatistical SignificanceElectronic Arts (Ea)
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3
Machine Learning11 questions · ~92 min
Decision Tree From ScratchHard

Implement a CART decision tree from scratch, including split selection, stopping rules, prediction, and evaluation.

Feature Engineeringmodel trainingSupervised LearningElectronic Arts (Ea)
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4
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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5
Product Sense6 questions · ~50 min
Engagement Metric Framework for New ModeMedium

Evaluates how you define and operationalize engagement metrics for a new multiplayer feature.

framework designproduct designEngagement MetricsElectronic Arts (Ea)
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6
Behavioral & Leadership11 questions · ~92 min
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7
More topics4 questions · ~33 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)
Model Evaluation MetricsHard

Select appropriate metrics for evaluating a model, considering task type, error costs, class balance, and deployment goals.

performance evaluationmodel performanceevaluation metricsElectronic Arts (Ea)
Optimizing Matchmaking AlgorithmsHard

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

ML RankingModel ServingRecommendation SystemsElectronic Arts (Ea)
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Hands-on SQL practiceWrite and run real queries in the editor. 2 drills · ~20 min
Rolling Transaction SQL and RankingHard
Practice
Practice drill

Calculate each user's 7-day rolling transaction average and daily spend rank using PostgreSQL window functions.

Window FunctionsDate FunctionsRankingGGreen ExchangeAANT SolutionRakuten
SQL Window Functions for TrendsMedium
Practice
Practice drill

Rank Delta Air Lines routes by monthly revenue using joins, aggregation, and a window function.

Window FunctionsLag/LeadJoinsDelta Air Lines
The finish line: interview-readyComplete all 50 questions plus 2 hands-on drills to finish this plan.