Electronic Arts creates next-level entertainment experiences that inspire players and fans around the world. Here, everyone is part of the story. Part of a community that connects across the globe. A place where creativity thrives, new perspectives are invited, and ideas matter. A team where everyone makes play happen.

EA - Data & Insights, AI Analytics Engineering

Data Science Intern

Team: EA - Data & Insights, AI Analytics Engineering 
Type: Internship (Full-time during term)

About the Team

EA is a global leader in digital interactive entertainment. The EA - Data & Insights, AI Analytics Engineering team plans, builds, and ships enterprise-grade data platforms, integrations, and analytics that power faster decision-making, unlock revenue opportunities, and improve business performance. We partner closely with product, engineering, and analytics teams across EA to deliver trusted, actionable insights.

Role Overview

You’ll join a hands-on, fast-moving team to build data and ML solutions with an emphasis on Python and software craftsmanship. You’ll write clean, well-tested code; wrangle large datasets; engineer features; train/evaluate models; and help move prototypes toward production in collaboration with senior engineers and architects.

What You’ll Do

• Build robust Python modules and notebooks for data ingestion, feature engineering, and model training (primarily with pandas, NumPy, and scikit-learn).
• Author clear, maintainable code using OOP, type hints, docstrings, and unit/integration tests; participate in code reviews and follow Git-based workflows.
• Explore datasets to define problem statements, create hypotheses, and conduct EDA with appropriate visualization and summary statistics.
• Implement and evaluate baseline and advanced ML models; select metrics, design experiments, and apply cross-validation.
• Apply solid SQL to extract/transform data; collaborate on building reliable data pipelines to support analytics and reporting use cases.
• Communicate results with crisp narratives, dashboards/plots, and reproducible notebooks; translate findings into product and business recommendations.
• Contribute to best practices in the team’s development lifecycle (automation, CI, documentation) and proactively suggest improvements.

Must‑Have Skills (Core Hiring Bar)

• Python mastery for data work: pandas, NumPy, scikit‑learn; writing reusable functions/classes; debugging and profiling; packaging basics.
• Strong coding fundamentals: data structures & algorithms, OOP, modular design, unit testing (pytest or similar), version control (Git), and code reviews.
• ML & DS foundations: supervised learning (linear/logistic regression, trees/ensembles), regularization, bias/variance, cross‑validation, feature scaling/encoding, and model evaluation (AUC/ROC, F1, RMSE/MAE, calibration).
• Statistics for data analysis: sampling, hypothesis testing, confidence intervals, distributions; ability to choose appropriate tests and interpret results.
• Solid SQL for data extraction/joins/aggregations and working knowledge of query optimization basics, along with proficiency in Git (GitHub/GitLab workflows, branching, pushing, merging).
• Data wrangling & EDA: handling missing/outliers, joins/pivots, time‑series/tabular transforms, clear visualizations (matplotlib/plotly) and narrative summaries.
• Problem solving & ownership: ability to define the problem, design experiments, deliver incremental value, and document decisions.
• Communication: concise written docs/notebooks and clear verbal explanations tailored to technical/non‑technical partners.

Good‑to‑Have Skills (Differentiators)

• Cloud & data platforms: exposure to Snowflake/BigQuery/Redshift; familiarity with AWS or Azure basics (e.g., S3/Blob, compute, IAM concepts).
• Pipelines & orchestration: experience with Airflow/Prefect or similar; understanding of batch vs. streaming concepts.
• Software craftsmanship extras: Makefiles/poetry/pip-tools, pre‑commit, linters/formatters, logging & observability, simple CLI tools.
• MLOps/productionization: model persistence (joblib/ONNX), reproducibility (seeds/environments), lightweight API serving (FastAPI/Flask), and tracking (MLflow/Weights & Biases).
• Advanced ML: gradient boosting (XGBoost/LightGBM/CatBoost), time‑series forecasting basics, recommendation, Neural Networks and NLP fundamentals.
• Big data: PySpark or Spark SQL for distributed transforms; understanding of partitioning and performance trade‑offs.
• Visualization & storytelling: dashboards in Plotly Dash/Streamlit; crafting stakeholder‑ready summaries.
• Competitive programming/problem-solving practice: experience with LeetCode, CodeChef, or similar platforms to strengthen algorithmic and coding proficiency.
• Other languages: basic R or SQL dialects; familiarity with JVM/C++/Scala is a plus.

About Electronic Arts

We’re proud to have an extensive portfolio of games and experiences, locations around the world, and opportunities across EA. We value adaptability, resilience, creativity, and curiosity. From leadership that brings out your potential, to creating space for learning and experimenting, we empower you to do great work and pursue opportunities for growth.