Decision Scientist

Workplace Type: Hybrid (Austin, TX or Vancouver, BC)

Reporting to: Director, Decision and Data Science

Office of the CXO

The Office of the CXO drives effectiveness across the EA Experiences organization with a focus on excellence in business operations, comprehensive fan intelligence, plus internal technology and business solutions. We are horizontal connectors empowering teams across the Experiences organization with the strategic prioritization, investments, resources, data, insights, and technology required to accelerate business outcomes.

Your Impact

You will transform complex data into actionable insights that improve fan experiences and support business decisions. Working with large-scale gaming and customer datasets, you'll partner with teams across EA to solve business challenges using statistical analysis, experimentation, and machine learning.

Responsibilities

• Analyze structured and unstructured datasets to identify trends and business insights.
• Build, validate, and improve predictive and prescriptive models using Python or R.
• Design and evaluate experiments to measure product and business outcomes.
• Develop dashboards and visualizations that communicate analytical findings.
• Partner with cross-functional teams to translate business questions into analytical solutions.
• Present recommendations through clear visualizations and business-focused storytelling.
• Apply responsible AI practices, including model validation, fairness, and bias monitoring.

Required Qualifications

• Master's degree in Statistics, Operations Research, Data Science, or a related field; equivalent experience considered.
• Experience using Python or R for statistical analysis and modeling.
• Experience with SQL and relational databases.
• Experience using visualization tools such as Tableau, Streamlit, or RShiny.
• Experience applying statistical methods to business problems.

Core Skills and Experience

• 2+ years applying statistical modeling in a business environment.
• Experience preparing, analyzing, and interpreting large datasets.
• Knowledge of experimentation and causal inference methods.
• Experience communicating analytical findings through dashboards and presentations.
• Familiarity with Git and cloud-based analytical environments.