We are looking for a data scientist to build and deploy models that help drive the sales of sports apparel for your favorite teams. This position is on the commerce customer marketing science group--they create the machine learning models to help get customers to the products they want. An example project is taking tens of millions of customers and predicting their team preferences using past behavior and sports seasonality. Another example is model which customers are likely to make a repeat purchase. This is a high impact role within the company due to the vast amount of marketing spend in the organization.

What you'll do:

• Train machine learning models for customer marketing purposes (CRM, ad bidding, etc)
• Deploy models into production to run at scale
• Use exploratory analysis to understand how business problems can be modeled
• Communicate with the marketing org to align business goals with science techniques and report results outwards

The ideal candidate would have:

• At least 3 years of experience in a role involving data (data scientist, machine learning engineer, data analyst, data engineer, etc.)
• The ability to write code in Python, R, or other data science languages
• An understanding of marketing science use cases (customer segmentation, lifetime value models, etc.)
• Experience with data science modeling techniques such as XGBoost, regressions, hyper-parameter tuning, and feature selection
• At least one past instance of having a model run continuously in production (like as an API or an automated batch process)
• A history of past positions where complex business goals were converted into data science problems
• Experience with using large and complex datasets spanning many tables
• Experience with running data science tasks in managed cloud environments (AWS, GCP, Azure)

The expected salary range for this role is $128,000 - $210,000. This is based on job-related knowledge, skills, and experience. This role is also eligible for the Fanatics Commerce annual bonus program and an equity award. This salary range is listed in USD and will be determined in part by a successful candidate’s geographic location.