Data Scientist - FIFA HD

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

EA SPORTS is one of the leading sports entertainment brands in the world, with top-selling video game franchises, award-winning interactive technology, fan programs, and cross-platform digital experiences.

The Challenge Ahead:

As a Data Scientist, you will help us improve our FIFA franchise by optimizing and personalizing our in-game economy and merchandising efforts. You are a technically-oriented individual, knowledgeable of how to use data and experimentation to solve product problems. You report to a central analytics and data science team but work directly with producers, designers, and product managers on the FIFA team to influence in-game content to help us create an ever more dynamic player journey in our titles.

What a Data Scientist does at EA:

• Translate business and product questions into ethical and mathematically sound experiments.
• Understand how to use principles of machine learning to help learn and tune in game experiences while staying aware of their consequences.
• Draft technical solutions when presented with a problem to a less technical audience.
• Prototype new technology that fits use cases/problems and validates against success criteria.
• Support peers with technical reviews, ensuring quality coding and deployment practices.

Requirements

• MS in Mathematics, Statistics, Economics, Computer Science, or related; or equivalent experience.
• 2 or more years of experience in data science, analytics, or a related area in a consumer products-oriented industry.
• Advanced SQL skills, including experience querying complex data sets using platforms like Apache Hive or BigQuery.
• Experience developing statistical and machine learning models using languages like Python or R and using tools like Sagemaker or Databricks.
• Experience with several methods of model deployment for use in production software.
• Knowledge of programming best practices, version control, and automation.
• Experience presenting and prioritizing with people at multiple levels of business and technical expertise.