About the Company

Electronic Arts is seeking a Data Science Intern to join their team!

Job Description

Electronic Arts is seeking a Data Science Intern to join their team!

Responsibilities

• Develop and maintain Python modules and Jupyter notebooks for data acquisition, feature engineering, and model training, primarily utilizing libraries such as pandas, NumPy, and scikit-learn.
• Create well-documented, maintainable code adhering to object-oriented programming principles, incorporating type hints, docstrings, and unit/integration tests. Participate in code review processes and follow established Git-based version control workflows.
• Investigate datasets to identify key problem areas, formulate hypotheses, and perform exploratory data analysis (EDA) using appropriate visualizations and summary statistics.
• Implement and evaluate both baseline and advanced machine learning models, selecting appropriate metrics, designing experiments, and applying cross-validation techniques.
• Utilize advanced SQL skills to extract and transform data, collaborating on the development of reliable data pipelines to support analytical and reporting requirements.
• Effectively communicate findings through clear narratives, informative dashboards and plots, and reproducible notebooks, translating insights into actionable product and business recommendations.
• Contribute to the enhancement of the team’s development lifecycle through automation, continuous integration (CI), and comprehensive documentation, while proactively suggesting process improvements.

Qualifications

• Demonstrated proficiency in Python for data manipulation and analysis, including extensive experience with pandas, NumPy, and scikit-learn, as well as the ability to write reusable functions/classes, perform debugging and profiling, and understand basic packaging concepts.
• Solid understanding of fundamental coding principles, including data structures and algorithms, object-oriented programming, modular design, unit testing (using pytest or a similar framework), version control (Git), and code review practices.
• Comprehensive knowledge of machine learning and data science foundations, encompassing supervised learning techniques (linear/logistic regression, decision trees/ensembles), regularization methods, bias-variance tradeoff, cross-validation strategies, feature scaling/encoding techniques, and model evaluation metrics (AUC/ROC, F1-score, RMSE/MAE, calibration).
• Strong statistical foundation for data analysis, including sampling techniques, hypothesis testing methodologies, confidence intervals, and various distributions, with the ability to select appropriate statistical tests and interpret the resulting outputs.
• Expertise in SQL for data extraction, joining, and aggregation, along with a working knowledge of query optimization principles. Proficiency in Git, including GitHub/GitLab workflows, branching, pushing, and merging.
• Experience in data wrangling and exploratory data analysis (EDA), including handling missing values and outliers, performing joins/pivots, conducting time-series and tabular transformations, creating clear visualizations using libraries such as matplotlib and plotly, and developing concise narrative summaries.
• Proven problem-solving skills and a sense of ownership, demonstrating the ability to define problems, design experiments, deliver incremental value, and document decisions effectively.
• Excellent communication skills, with the ability to create concise written documentation/notebooks and provide clear verbal explanations tailored to both technical and non-technical audiences.

Skills

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Other Information

Internship Duration: 3 months
Working Days: 5 Days
Eligibility: Fresher

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