Electronic Arts

The Data & Insights (D&I) team harnesses the power of data to create transformative insights and solutions to EA game teams and players. D&I teams are involved in every aspect of data, driving strategy and governance; build powerful data and AI applications; and develop personalization, experimentation, and analytics tools, and more. When you join D&I, you're joining a team of passionate experts working to promote more profound understanding, better ways of working, and more meaningful experiences for our players.

As the gaming industry shifts towards a live service-driven model, creating an engaging Ads experience and connecting relevant brands / advertisers to players is the key to EA's success. The AdTech team within EADP's Dynamic Experience group is on a journey to build industry-leading solutions that empower e2e Ads lifecycle management workflows and performant, scalable and available services.

 As an ML Engineer II, you will report to the Director of Engineering and will contribute to the backend services and data infrastructure powering this platform.

Responsibilities:

• You will develop and implement machine learning models for prediction, classification, and optimization.

• You will analyze large datasets using statistical methods to extract insights.

• You will build and maintain real-time data pipelines using tools like Apache Kafka, Spark Streaming, or Flink.

• You will collaborate with engineering and product teams to integrate models into production.

• You will conduct A/B testing and validate model performance.

• You will communicate findings through reports and presentations.

Qualifications:

• 4+ years of experience in data science or machine learning

• Proficient in Python and libraries such as NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow

• Strong understanding of statistical modeling and hypothesis testing

• Experience with real-time data processing frameworks

• Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes)

• Strong problem-solving and communication skills

Bonus:

• Experience deploying ML models in production

• Knowledge of MLOps tools (MLflow, Airflow, etc.)

• Experience with time-series modeling and anomaly detection

• Contributions to open-source or published research