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:

 •  Develop and optimize data pipelines to extract, process, and store structured and unstructured data for AI/ML applications.

 •  Perform data mining and feature engineering, ensuring high-quality datasets for machine learning models.

 •  Apply machine learning techniques to develop predictive models, improve classifiers, and extract actionable insights.

 •  Ensure data integrity and governance by cleansing, validating, and preprocessing large-scale datasets.

 •  Analyze complex datasets to uncover patterns, trends, and business insights, driving data-driven decision-making.

 •  Build, train, and test ML models, ensuring scalability, accuracy, and efficiency.

 •  Optimize model performance and inference pipelines using best practices in software engineering and MLOps.

 •  Work with Python, SQL, and distributed computing frameworks (Spark, Snowflake) to manage large-scale data processing.

 •  Collaborate with cross-functional teams (Business, Engineering, and IT) to align data science solutions with company objectives.

 •  Stay up-to-date with emerging AI/ML advancements, implementing the latest research and best practices in production.

 •   Communicate findings and insights effectively to technical and non-technical stakeholders, driving adoption of data-driven solutions.

Qualifications:

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

 •  5+ years of experience in data science, analytics, ML engineering, with a strong focus on building scalable solutions.

 •  3+ years of hands-on experience developing, deploying, and optimizing Machine Learning models in production environments.

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

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

 •  Strong knowledge of feature engineering, model evaluation, and hyperparameter tuning for predictive modeling.

 •  Experience with statistical programming (Python, R) and familiarity with Scala, Java, or C++ for performance optimization.

 •  Proficiency in SQL for complex queries, data extraction, analysis, and profiling.

 •  Experience with cloud platforms (AWS, GCP, Azure) and distributed data processing tools like Spark, Snowflake, or Databricks.

 •  Deep understanding of classical ML algorithms (k-NN, Naïve Bayes, SVM, Decision Forests, Gradient Boosting, etc.).

 •  Exposure to deep learning architectures such as CNNs, RNNs, and Transformers is a plus, with a strong understanding of their applications and optimizations being highly desirable.

 •  Strong grasp of applied statistics, including probability distributions, regression analysis, statistical tests, and maximum likelihood estimation.

 •  Good to have knowledge of vector databases and experience with NLP and LLMs, including text annotation and information extraction