Data Scientist III - FES

About Us

Fanatics is building a leading global digital sports platform. We ignite the passions of global sports fans and maximize the presence and reach for our hundreds of sports partners globally by offering products and services across Fanatics Commerce, Fanatics Collectibles, and Fanatics Betting & Gaming, allowing sports fans to Buy, Collect, and Bet. 

Through the Fanatics platform, sports fans can buy licensed fan gear, jerseys, lifestyle and streetwear products, headwear, and hardgoods; collect physical and digital trading cards, sports memorabilia, and other digital assets; and bet as the company builds its Sportsbook and iGaming platform. Fanatics has an established database of over 100 million global sports fans; a global partner network with approximately 900 sports properties, including major national and international professional sports leagues, players associations, teams, colleges, college conferences and retail partners, 2,500 athletes and celebrities, and 200 exclusive athletes; and over 2,000 retail locations, including its Lids retail stores. Our more than 22,000 employees are committed to relentlessly enhancing the fan experience and delighting sports fans globally.

About The Team

We are the Fan Ecosystem Data team, responsible for enhancing decision-making and innovation across the entire Fanatics ecosystem through data and analytics. We build products that turn disparate data streams into real-time actionable insights, empowering teams to unlock greater value for our customers and stakeholders across every Fanatics surface.

We are seeking a Data Scientist III to drive predictive insights through a cross-vertical enterprise lens at the intersection of all our Fanatics businesses (Advertising & Loyalty, Betting & Gaming, and eCommerce & Collectibles). You will translate fan signals into measurable enterprise value through advanced segmentation, targeting, and the Next Best Action models that power personalization across the entire fan lifecycle.

Responsibilities

• Develop a deep understanding of the end-to-end customer lifecycle across all Fanatics lines of business, building a unified view of fan behavior across commerce, betting, collectibles, and media. 
• Build, deploy, and iterate on Lifetime Value (LTV) models, including probabilistic frameworks and retention-based approaches, to improve enterprise customer segmentation and cross-sell opportunities. 
• Design and implement churn, reactivation, and propensity models that feed directly into loyalty, CRM, and promotional decision-making across business units. 
• Apply causal inference and uplift modeling to measure true incremental value of cross-BU promotions and personalization strategies. 
• Develop analytical and science frameworks that facilitate enterprise trade-offs and executive decision-making across business units. 
• Build strong relationships with stakeholders across Advertising & Loyalty, Betting & Gaming, and eCommerce & Collectibles to drive an ecosystem-wide data mindset. 

Experience And Skills

• A minimum of 3-5 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.
• Hands-on experience with LTV or customer value modeling (probabilistic frameworks, retention curve modeling, or cohort-based CLV) in a consumer, subscription, or transactional environment. 
• Proven experience with experiment design in loyalty or promotional contexts. 
• Strong grasp of survival analysis and its application to customer churn and retention modeling. 
• 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.

Preferred But Not Required

• Prior experience with multi-brand or multi-product customer data, identity resolution, or unified customer modeling across verticals. 
• 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.