Data Analyst - FIFA
11 days ago
At EA, we inspire the world to play by developing, publishing, and distributing the world's best games. Our EA Vancouver studio is looking for a Data Analyst to support our FIFA franchise. Reporting to the lead analyst on FIFA's live services, you'll work with the live product & content teams along with colleagues in analytics around the world. We're looking for someone with a passion for diving into data to uncover insights. You'll not only be a data explorer and modeler, but also a data storyteller who will help guide the company to create extraordinary gaming experiences for our players.
Examples of projects you may work on include: assessing campaign content impact, reporting on content performance, projecting engagement patterns, and tuning mode features and player rewards.
What you'll do on our team:
- Receive questions from our partners, determine the best ways that our data can help answer them, and provide insights through dashboards, reports, or presentations.
- Develop insights to help our partners make informed decisions about in-game campaign planning.
- Work with game producers to understand their needs and translate analysis into realistic recommendations.
- Explore player behaviour patterns in our data to identify opportunities to improve the player experience.
- Model trends in our data to predict the ways in which new content and campaigns will affect our performance.
- Present analyses and findings to our partners in a clear and understandable way.
What we're looking for:
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- A degree in Business, Statistics, Mathematics, Economics, Data Science, Analytics, or a related field.
- 2+ years of experience in an analytics role using data to help guide decisions in a consumer products-oriented industry (e.g. gaming, entertainment, e-commerce).
- Excellent SQL querying skills.
- Proficiency analyzing large datasets in a spreadsheet program or in a programmatic analysis tool (e.g. Python, R).
- Experience with data visualization applications (e.g. Tableau, R Shiny).
- Experience in statistical modeling (e.g. regression, clustering, predictive modeling).