Description & Requirements

Location: Austin (Hybrid Role)

Central Technology is the force multiplier that accelerates creative opportunity and progress at EA. We build and operate the platforms, AI-driven tools, live services, security capabilities, and infrastructure that support EA's global scale and help create safe, fair, and trusted player experiences.

The Gameplay Security, or GPS, team protects the integrity of EA games, player accounts, and connected gameplay experiences. We work across cheating, botting, gameplay exploitation, account boosting, mass account registration, account abuse, payment fraud, chargebacks, and other forms of suspicious behavior that can impact fair play and player trust.

We are looking for a Senior Data Scientist to join the GPS team. You will use gameplay telemetry, player behavior, account lifecycle data, registration signals, transaction data, enforcement outcomes, and game event data to build models, features, and analytical approaches that help detect and reduce abuse across EA's ecosystem.

This role is ideal for a data scientist who is analytical, curious, collaborative, and comfortable working with complex data in an adversarial environment where player behavior and abuse patterns evolve over time.

Responsibilities

• Lead the data science work across the model lifecycle, from exploratory analysis and feature development through evaluation, monitoring, and partnership with engineering on deployment.
• Design and develop statistical and machine learning models to detect cheating, botting, account boosting, mass account creation, account abuse, payment fraud, and other suspicious gameplay or platform behaviors.
• Build and improve features, risk signals, and detection approaches using gameplay telemetry, player behavior, account lifecycle, registration, transaction, and game event data.
• Continuously evaluate and improve detection effectiveness by measuring model performance, reducing false positives, and adapting to evolving abuse patterns.
• Collaborate with game teams, security engineers, product partners, fraud stakeholders, and anti-cheat teams to support data-informed detection and enforcement strategies.
• Communicate analytical findings clearly to technical and non-technical stakeholders, including model performance, limitations, tradeoffs, confidence levels, and recommended actions.

Required Qualifications

• 5+ years of experience in data science, machine learning, applied statistics, risk modeling, security analytics, fraud detection, or a related analytical field.
• Strong experience with Python or R and advanced SQL.
• Experience building statistical or machine learning models using large-scale behavioral, event, transaction, account, or telemetry data.
• Strong understanding of model evaluation, including precision and recall tradeoffs, false positive analysis, thresholding, noisy labels, and delayed outcomes.
• Ability to work cross-functionally with engineering, product, operations, security, or game teams and explain complex analytical findings clearly.

Preferred Qualifications

• Experience in gaming, gameplay security, anti-cheat, fraud detection, trust & safety, cybersecurity, bot detection, or another adversarial domain.
• Experience with one or more of the following areas: cheating, botting, account boosting, mass account registration, fake account detection, account abuse, payment fraud, chargebacks, or abnormal gameplay behavior.
• Experience developing features from behavioral, transactional, registration, account lifecycle, or gameplay telemetry data.
• Experience partnering with engineering teams to operationalize models, detection signals, dashboards, monitoring workflows, or data pipelines.
• Familiarity with cloud, data, or ML platforms such as AWS, GCP, Spark, Databricks, Snowflake, Hive, Kafka, Airflow, Kubernetes, or similar technologies.

Post To

External careers site, Internal careers site

LinkedInID

1449