Data Scientist III
FanaticsAbout the role
Fanatics Betting & Gaming (FBG) is part of Fanatics’ global digital sports platform, uniting commerce, collectibles, and betting into one ecosystem. Our mission is to build the world’s most rewarding, trusted, and fan-centric betting experience. The Data Science team powers this mission through experimentation, predictive modeling, and applied machine learning that inform every key decision across the business.
You will join a high-impact data science team that builds models and analytics driving acquisition, retention, and monetisation across our emerging products. This role focuses on experimentation, prediction, and decision support for product, growth, and leadership teams.
Responsibilities
- Partner with product and CRM teams to define hypotheses and design controlled experiments.
- Build predictive models for churn, LTV, segmentation, and cross-sell propensity.
- Analyse customer journeys, campaign impact, and betting behaviour patterns.
- Support live testing and deploy models in production environments.
- Create dashboards and automated insight pipelines for key KPIs.
- Present clear, data-driven recommendations to stakeholders across London, Dublin, and U.S. offices.
- Contribute to internal standards for reproducible analysis, version control, and documentation.
Qualifications
- 7-10 years of experience as a Data Scientist or Quantitative Analyst in tech, gaming, or finance.
- Proficiency in Python (pandas, scikit-learn, or equivalent) and SQL.
- Strong applied statistics and experimental design knowledge.
- Hands-on experience with modern data warehouses (Snowflake, BigQuery, or Redshift).
- Ability to communicate quantitative findings to non-technical audiences.
- Degree in a quantitative field (Statistics, Computer Science, Economics, Engineering, or similar).
Preferred
- Experience in sports betting, iGaming, or fintech.
- Familiarity with A/B testing at scale, uplift modeling, and causal inference.
- Exposure to ML pipeline orchestration (Airflow, Databricks, MLflow).
- Experience working in AWS or GCP
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