Data Science Intern

About the Role

As a Data Science Intern at Electronic Arts (EA), you will gain hands-on experience in developing custom machine learning models to address real-world challenges within EA's games. You will have the opportunity to build scalable, real-time solutions and contribute to driving player engagement across popular titles. This role is ideal for individuals who enjoy solving complex computing problems, are passionate team players, and thrive in a fast-paced, evolving development environment.

Location: Hyderabad

Key Responsibilities

• Work with Product Managers, senior engineers, and peers to understand requirements, prototype, and build large-scale distributed applications.
• Develop and implement custom machine learning models.
• Clean, preprocess, and analyze large datasets, ensuring data quality and integrity.
• Address and manage imbalanced datasets to optimize model performance.
• Deploy and host machine learning models, ensuring scalability and reliability in production environments.
• Monitor and maintain models in production, implementing feedback loops for continuous improvement.
• Identify and mitigate biases in models to ensure fairness and accuracy.
• Collaborate with cross-functional teams to understand business needs and translate them into technical requirements.
• Document processes and model performance, providing insights and recommendations.

Requirements & Tech Stack

• Proficiency in Python or R.
• Experience with libraries such as TensorFlow, PyTorch, or Scikit-learn.
• Familiarity with deploying models using cloud platforms (AWS, GCP, or Azure).
• Good understanding of LLM architecture, fine-tuning, and training LLMs.
• Robust understanding of AI agents.

Qualifications

• Bachelor's/Master's degree in Computer/Software Engineering, Computer Science, or a related concentration, or an equivalent combination of education and experience.
• A solid foundation in Computer Science fundamentals, algorithms, design patterns, CI/CD processes, and data structures.
• Strong understanding of data preprocessing techniques and handling imbalanced datasets.
• Knowledge of model evaluation metrics and techniques to identify and reduce bias.
• Excellent problem-solving skills and attention to detail.
• Strong communication skills, with the ability to explain complex concepts to non-technical stakeholders.
• Superb ability to take high-level direction, work through ambiguity, and drive to execution in an iterative model.
• Superb ability to translate business requirements into technical design & architecture.
• Ability to communicate ideas clearly and effectively.