Sr Data Scientist

Electronic Arts is looking for a Senior Data Scientist to join their Central Technology team, specifically in the Security department. The successful candidate will lead end-to-end data science initiatives, design and develop statistical and machine learning models to detect cheating, fraud, and other emerging platform risks.

Responsibilities:

• Lead end-to-end data science initiatives, from problem definition and exploratory analysis through model development, evaluation, deployment, and monitoring.
• Design and develop statistical and machine learning models to detect cheating, fraud, account abuse, botting, suspicious gameplay, and other emerging platform risks.
• Build scalable features, risk signals, and detection frameworks using gameplay telemetry, player behavior, account, transaction, and operational data.
• Investigate complex abuse patterns, translate insights into models, rules, dashboards, and recommendations, and continuously improve detection quality by optimizing model performance and reducing false positives.
• Establish best practices for model evaluation, monitoring, drift detection, and impact measurement while partnering with engineering teams to productionize data science solutions.
• Collaborate with product, security, anti-cheat, fraud, game, and operations teams to develop data-driven prevention and enforcement strategies.
• Mentor junior data scientists, promote reusable data science practices, and communicate analytical findings, model tradeoffs, and business impact to technical and non-technical stakeholders.

Required Qualifications:

• 7+ years of experience in Data Science, Machine Learning, Applied Statistics, Fraud Detection, Security Analytics, Trust & Safety, or a related analytical field.
• Strong proficiency in Python or R and advanced SQL.
• Experience leading end-to-end machine learning projects from ambiguous problem definition through production deployment.
• Expertise building statistical or machine learning models using large-scale behavioral, transactional, telemetry, account, or security datasets.
• Strong understanding of model evaluation, including precision/recall tradeoffs, threshold optimization, calibration, false positives, monitoring, and model performance measurement.
• Experience engineering features from complex, multi-source datasets and translating business or security problems into scalable analytical solutions.
• Proven ability to partner cross-functionally with engineering, product, security, fraud, or operations teams to deliver production-ready models, dashboards, and decision-support tools.
• Experience mentoring technical teammates and effectively communicating complex analytical insights to diverse audiences.

Benefits:

• Comprehensive benefits package, including health insurance, mental wellness support, retirement savings, paid time off, family leave, free games, and more.