Senior Data Scientist – Commerce Supply Chain Optimization

Fanatics Commerce is the global leader in licensed sports merchandise, operating a vertically integrated platform that designs, manufactures, and delivers officially licensed apparel, jerseys, headwear, and collectibles for major leagues, teams, and events worldwide. With more than 900 e-commerce sites and a global omnichannel presence across digital, in-venue, and retail, Fanatics Commerce reaches fans in over 180 countries and powers official fan experiences for many of the world’s most iconic sports properties.

At Fanatics, we bring our BOLD Leadership Principles to life every day - building championship teams, obsessing over fans, acting with entrepreneurial speed, and delivering with a determined and relentless mindset.

As we continue to scale our global platform, the Senior Data Scientist, Supply Chain & Inventory Optimization will play a critical role in maximizing supply chain efficiency and inventory financial performance through ML-driven modeling, pricing optimization, and demand forecasting.

This role sits within a high-impact team responsible for product lifecycle modeling, pricing and promotional optimization, clearance management, demand forecasting, network simulation, and inventory sourcing, allocation, and balancing. The Senior Data Scientist will bring a rigorous, ML-driven mindset across operational modeling, forecasting, pricing, and product strategy - moving fluidly across domains in a collaborative, fast-moving team culture with a strong emphasis on delivering measurable business impact amid frequently shifting requirements.

The Senior Data Scientist delivers business and fan impact through BOLD leadership and execution excellence, leveraging data, automation, and AI-enabled insights.

Success is measured by the ability to deliver results through BOLD capabilities and measurable outcomes.

• Partner cross-functionally with engineering, product, and operations teams to frame complex supply chain and inventory problems and translate analytical models into decisions and tools that get adopted.
• Communicate modeling tradeoffs and results clearly to both technical and non-technical audiences, building trust and shared understanding across teams.
• Contribute to a collaborative team culture by sharing methodologies, reviewing peers' work, and supporting the growth of junior data scientists.

• Build demand forecasting and product performance models that ensure the right products are available to fans at the right time and place.
• Develop pricing and promotional optimization models that improve the fan value experience while protecting financial performance.
• Support clearance management strategies that minimize inventory surplus without compromising the fan-facing product assortment.
• Deliver decision-support tools and stakeholder-facing reporting that translate complex models into actionable insights for operations and product teams.

• Design, test, and deploy time series models for demand forecasting, product lifecycle tracking, and performance analytics using both classical (ARIMA, exponential smoothing) and ML-based approaches (XGBoost, LSTM, DeepAR).
• Build and refine pricing, clearance, and network optimization models using discrete optimization techniques - including MIP, constraint solvers, and genetic algorithms - alongside simulation and heuristic methods.
• Apply exploratory data analysis and statistical methods to uncover performance drivers, engineer predictive features, and inform model design decisions.
• Develop scalable pipelines and automation tools using Python, Spark, and cloud infrastructure to operationalize models at scale.

• Own the full model development lifecycle - from problem framing and data engineering through training, evaluation, deployment, and monitoring - across supply chain and inventory initiatives.
• Develop and maintain predictive models spanning forecasting, classification, regression, clustering, and segmentation in a production or operational environment.
• Deliver consistently against business objectives in a fast-paced environment with frequently shifting priorities and requirements.
• Take accountability for the measurable business impact of deployed models, tracking outcomes and iterating based on real-world performance.

• Apply AI and technology to improve efficiency, quality, and outcomes
• Use data and digital tools to inform decisions and enhance performance
• Demonstrate curiosity and adaptability in adopting new technologies and ways of working
• Contribute to a culture of innovation and continuous improvement

• 6+ years of experience building and deploying predictive models - spanning supervised learning (regression, classification), unsupervised learning (clustering,…