Data Scientist

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 Sports book 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

Fanatics Collectibles is a new company operating at the intersection of collectibles, culture, and technology. Backed by world‑class investors, operators, and sports & entertainment partners, we are building the global leader in next‑generation collectibles. As part of the broader Fanatics ecosystem, we leverage a digital sports platform that spans over 900 sports properties and engages more than 81 million fans. Together, we are reimagining every part of the collectibles experience for fans, partners, and creators.

As a Data Scientist, you will support the development of analytical models, experimentation, and reporting that help improve our products and inform business decisions. Working closely with senior data scientists, product managers, engineers, and analysts, you will apply statistical methods and machine learning techniques to analyze data, uncover insights, and contribute to data‑driven product improvements. This role is ideal for someone early in their data science career who is eager to expand their technical skills while gaining experience in a fast‑paced, product‑focused environment.

• Partner with Product, Engineering, and Analytics teams to understand business questions and support data‑driven decision making.
• Build, validate, and maintain analytical models and data pipelines under the guidance of senior team members.
• Perform statistical analyses and exploratory data analysis to identify trends, patterns, and opportunities.
• Support A/B testing initiatives by assisting with experiment design, data validation, statistical analysis, and communicating results.
• Develop dashboards and reports using tools such as Sigma or Looker to help stakeholders monitor product performance and key business metrics.
• Write efficient SQL queries and work with cloud‑based data warehouses such as Snowflake or Big Query to prepare and analyze data.
• Use Python or R to automate analyses and develop reproducible analytical workflows.
• Leverage AI‑assisted development tools (such as Claude or OpenAI) and engineering platforms (Git Hub, Airflow) to improve productivity and collaboration.
• Collaborate with senior data scientists to improve existing models and analytical processes while continuing to build technical expertise.
• Clearly communicate analytical findings and recommendations to technical and non‑technical stakeholders.

• Bachelor’s degree in Statistics, Mathematics, Computer Science, Data Science, or another quantitative field, or equivalent practical experience.
• 1–3 years of experience in data science, analytics, or a related quantitative role.
• Working knowledge of SQL and experience querying large datasets.
• Proficiency in Python or R for data analysis.
• Foundational understanding of statistics, hypothesis testing, regression analysis, and experimental design.
• Familiarity with machine learning concepts and common libraries such as scikit‑learn.
• Strong analytical thinking, curiosity,…