Electronic Arts

We're hiring a Senior Analyst for our Data and Insights team, where our job is to use statistical skills, research methodologies, and data analysis to represent the voice of the player. Reporting to a Director of Analytics, you will partner with developers across multiple ARPG titles in the live service and games as a platform space to provide impactful analysis with relevant takeaways. In addition to working with our studio partners, you will also have opportunities to collaborate and share with other data experts among the larger data analytics community at EA. As a team of analysts and researchers, we understand how important teaching, learning, and growth are.

Responsibilities:

• Lead analytics projects for live-service titles and games as a platform, from concept through to actionable recommendations, ensuring insights directly inform and support launch and ongoing operations

• Use player data to perform behavioral analysis, clustering, predictive modeling, hypothesis testing, and more, with a focus on understanding engagement with dynamic game worlds, user-generated experiences, and ongoing content releases

• Partner with designers, product managers, producers, and engineers to establish the data needed from a game to understand how players engage with its features and systems

• Utilize third-party data to make inferences about the competitive landscape and inform decision-making

• Engage with peers to develop and share best practices, and establish subject matter expertise within the analytics community

• Develop dashboards and visualizations to monitor important game features and business health, applying automation and insights when relevant

Requirements:

• 3+ years of experience applying quantitative or statistical methods to produce insights from data

• Deep understanding of live-service games, user-generated content, or complex player ecosystems (e.g., Roblox, Minecraft, Fortnite, or similar)

• Experience presenting technical findings to audiences with varying backgrounds

• SQL proficiency and the ability to extract data from large data sources with differing structures

• Proficiency with a statistical analysis tool (such as R or Python) to perform analysis

• Proficiency with a data visualization tool (such as Looker) to build clear, user-friendly data visualizations, tables, and dashboards

• Familiarity with version control and reproducible research best practices