Electronic Arts

Electronic Arts Inc. is a global leader in interactive entertainment. We develop games, content and online services across platforms. We have a broad portfolio of brands that span the most popular genres.

We exist to Inspire the World to Play. We create extraordinary new game experiences for our millions of players everywhere by bringing together experienced people that combine creativity, innovation, and passion. We celebrate diversity and inclusion and aim to create great experiences for our employees as often as our players. We're looking for people to come to the office or Zoom meeting excited to work and ready for some fun. The Data Science engineer will report to Sr Manager Engineering responsible for Data & Insights. 

Join us in driving the next revolution in games.

Responsibilities:

 •  Perform data exploration, mining, and feature engineering to create high-quality datasets for model development.

 •  Apply machine learning and statistical modeling techniques to build predictive and prescriptive models that generate actionable business insights.

 •  Ensure data integrity and governance through robust validation, preprocessing, and quality assurance processes.

 •  Analyze complex datasets to identify patterns, trends, and opportunities that drive data-informed business decisions.

 •  Build, train, validate, and deploy scalable machine learning models, ensuring optimal accuracy and performance.

 •  Implement MLOps best practices for model deployment, versioning, monitoring, and retraining across production environments.

 •  Utilize Python, SQL, and distributed data processing frameworks such as Spark, Snowflake, or Databricks to handle large-scale data workflows.

 •  Collaborate closely with cross-functional teams (Engineering, Product, and Business) to align models and insights with strategic goals.

 •  Evaluate and integrate cutting-edge AI/ML research, staying abreast of advancements in deep learning, transformers, and LLMs.

Qualifications:

 •  Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, Statistics, or a related field (or equivalent professional experience).

 •  7+ years of experience in data science, analytics, or ML engineering with a strong record of deploying scalable ML solutions.

 •  5+ years of hands-on experience developing, deploying, and optimizing machine learning and deep learning models in production.

 •  Expert proficiency in Python and deep learning frameworks (TensorFlow, PyTorch, Scikit-learn).

 •  Proven experience applying machine learning to time-series data for forecasting, anomaly detection, and pattern recognition.

 •  Strong familiarity with RNN, LSTM, and Transformer architectures, including self-attention mechanisms for sequence modeling.

 •  Experience in handling large-scale and high-dimensional datasets using distributed data processing tools (Spark, Snowflake, Databricks).

 •  Strong experience with ML deployment frameworks (MLflow, Kubeflow, SageMaker, Vertex AI).

 •  Expertise in feature engineering, model evaluation, and hyperparameter tuning.

 •  Solid understanding of statistical modeling, including probability distributions, regression analysis, and hypothesis testing.

 •  Proficiency in SQL for advanced data extraction, profiling, and performance tuning.

 •  Experience with cloud platforms (AWS, GCP, Azure) and modern data ecosystems.

 •  Knowledge of vector databases, NLP, and LLMs, including text annotation and information extraction, is an added advantage.

 •  Strong communication skills with the ability to translate complex models into business impact.