Data Scientist I

Fanatics is building a leading global digital sports platform, and they are seeking a Data Scientist I to join their growing Data Science organization. This role will focus on creating insights to drive business growth and will involve close collaboration with senior commercial stakeholders.

What you'll do:

• Develop a deep understanding of the iGaming ecosystem, encompassing both Sportsbook and Casino products, operations, and player behavior
• Build strong relationships with stakeholders across both Sports and Casino business units, serving as a key technical contributor to driving total commercial performance
• Collaborate with Data Scientists and Analysts to implement and refine scalable solutions for complex iGaming business problems
• Contribute to the development, deployment, and iterative improvement of automated personalization and promotion engines across the iGaming suite
• Remain current on technology, machine learning trends, and competitive shifts within the global sports betting and gaming industry

What they're looking for:

• 0-2 years proven experience in a data science or advanced analytics role
• Degree in a quantitative field, e.g., Mathematics, Physics, Statistics, Engineering, or Computer Science, Economics
• Strong SQL proficiency and strong proficiency in Python, with experience building and validating machine learning models
• Strong understanding of statistics concepts such as regression, clustering, and A/B testing in high-growth environments
• Demonstrated ability to partner with stakeholders, earning trust through data-driven insights and clear communication
• Outcome-oriented and data-driven; comfortable navigating fast-paced, high-growth environments
• Experience experimenting with Generative AI solutions (e.g., LLM-based analysis, automation, or insight generation) and identifying opportunities to apply them within a commercial analytics environment
• Previous experience in iGaming (Sportsbook or Casino) is a significant plus
• Expertise in experimental design and causal inference, including A/B/n testing, quasi-experiments, and uplift modeling to determine true incremental value