Electronic Arts

The Data & Insights (D&I) team harnesses the power of data to deliver transformative insights and solutions to EA game teams and players. D&I teams are involved in every aspect of data, driving strategy and governance; building powerful data and AI applications; and developing personalization, experimentation, and analytics tools, and more. When you join D&I, you're joining a team of passionate experts working to enable more profound understanding, better ways of working, and more meaningful experiences for our players.

We're looking for a sharp, curious, and collaborative Data Scientist to join our Growth Analytics team. You'll work closely with media buyers to build models and experiments that inform our marketing strategy. Your insights will shape how we allocate budget, measure impact, and forecast performance across digital channels.

You'll join a tight-knit team of five data scientists and will report directly to the Senior Manager of Data Science.

This is a hybrid role with an expectation of 3 in-office days per week. The position is open to candidates based near any EA office in Canada.

Responsibilities:

• Build Bayesian Hierarchical Media Mix Models (MMMs) to quantify the contribution of marketing channels

• Design and analyze geo-based incrementality experiments and user-level re-engagement tests to measure the causal impact of marketing spend

• Lead the design and analysis of retargeting experiments, applying A/B testing principles to evaluate incremental impact and guide campaign optimization

• Develop budget optimization tools to support strategic decision-making

• Create forecasting models (ie: Prophet) to project game performance

• Translate model insights into actionable recommendations, collaborate closely with media buyers and presenting results and insights to stakeholders

Qualifications:

• Master's degree in a quantitative field (e.g., Statistics, Econometrics) and 2–5 years of experience in data science or analytics, ideally focused on marketing measurement or experimentation

• Familiarity with MMMs, causal inference techniques (e.g. CausalImpact, Fixed Effect Regression), and experimental design

• Strong experience with Python/R and SQL

• Comfort working in a Bayesian modeling framework

• Ability to communicate technical results to non-technical stakeholders