Senior Data Scientist – Commerce Supply Chain Optimization

Fanatics Commerce • San Mateo, CA

13h • $170,000 - $210,000

About The Position

Fanatics Commerce is seeking a Senior Data Scientist, Supply Chain & Inventory Optimization to play a critical role in maximizing supply chain efficiency and inventory financial performance through ML-driven modeling, pricing optimization, and demand forecasting. This role is part of a high-impact team focused on product lifecycle modeling, pricing and promotional optimization, clearance management, demand forecasting, network simulation, and inventory sourcing, allocation, and balancing. The ideal candidate will bring a rigorous, ML-driven mindset to operational modeling, forecasting, pricing, and product strategy, working 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 will deliver business and fan impact through BOLD leadership and execution excellence, leveraging data, automation, and AI-enabled insights.

Requirements

• 6+ years of experience building and deploying predictive models — spanning supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), and time series forecasting — in a production or operational environment.
• Strong proficiency in Python (Pandas, Scikit-learn, NumPy) and SQL, with hands-on experience using Spark or another distributed computing framework for scalable data processing.
• Deep expertise in time series forecasting using both classical methods (ARIMA, exponential smoothing) and ML-based approaches (XGBoost, LSTM, DeepAR), including rigorous model evaluation practices.
• Demonstrated experience applying discrete optimization techniques — including mixed-integer programming, constraint solvers, and genetic algorithms — to real-world business problems such as pricing, clearance, or network design.
• Familiarity with simulation-based modeling and tradeoff analysis for operational or supply chain decision-making.
• Experience with feature engineering and EDA-driven insight development to inform model design and uncover business performance drivers.
• Strong communication skills with the ability to clearly explain modeling tradeoffs and results to both technical and non-technical stakeholders.
• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Operations Research, or a related field.

Nice To Haves

• Experience with data visualization and BI tools such as Superset or Tableau for stakeholder-facing reporting.
• Prior experience in retail, e-commerce, or supply chain domains.
• Experience mentoring or guiding junior data scientists.

Responsibilities

• 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.

Benefits

• The salary range represents base pay only and does not include short-term or long-term incentive compensation. When determining base pay as part of a final compensation package, we consider several factors such as location, experience, qualifications, and training. For information about our benefits, please visit https://benefitsatfanatics.com/

What This Job Offers

• Job Type: Full-time
• Career Level: Senior