Senior Data Scientist (Retail)

PGA Tour • Roswell, GA

2d • Onsite

About The Position

At PGA TOUR Superstore, we’re always looking for enthusiastic, self-motivated, flexible individuals who will share a passion for helping transform our business. As one of the fastest growing specialty retailers, we’re dedicated to hiring selfless team players from different backgrounds to influence the growth of our organization. Part of the Arthur M. Blank Family of Businesses, PGA TOUR Superstore continuously strives to create a family culture for our Associates – driven by our vision to inspire people through golf and tennis. Position Summary PGA TOUR Superstore is seeking a Senior Data Scientist – Retail, to develop and apply advanced analytics that drive forecasting, inventory, and merchandising decisions across the business. This role is focused on building accurate, scalable demand forecasts and translating those insights into actionable inventory and operational strategies. You will work closely with Finance and Merchandising to define problems, build predictive models, and deliver insights that improve decision-making. This includes forecasting demand and translating results into actionable strategies for inventory, replenishment, pricing, and operations. The ideal candidate is comfortable working with large, complex retail datasets and can translate data into clear insights and recommendations for non-technical stakeholders. This role requires strong collaboration with cross-functional partners and a focus on delivering practical, high-impact solutions that improve business outcomes.

Requirements

• 5+ years of experience in data science, machine learning, or advanced analytics, preferably in retail, CPG, or a similar transactional business.
• Hands-on experience with forecasting techniques such as ARIMA, ETS, XGBoost, Random Forest, and/or LSTM, with a strong understanding of when and how to apply each.
• Strong understanding of statistical modeling, machine learning, and predictive analytics, including model evaluation, feature engineering, and handling real-world data limitations.
• Proficiency in Python and SQL for data analysis, model development, and working with large-scale datasets.
• Experience working with large, complex datasets and modern data platforms; experience with Snowflake or Databricks preferred.
• Experience with machine learning frameworks such as scikit-learn, PyTorch, TensorFlow, or similar tools.
• Experience using version control systems (e.g., Git/GitHub) for collaborative development and reproducible workflows.
• Experience translating analytical outputs into business decisions, particularly in partnership with Finance, Merchandising, or Operations teams.
• Familiarity with inventory, supply chain, or optimization concepts (e.g., safety stock, service levels, or replenishment) preferred.

Responsibilities

• Develop and own demand forecasting models across products, stores, and channels to support Finance and Merchandising planning, selecting and applying appropriate forecasting techniques based on business needs and data characteristics.
• Translate forecasts into inventory and replenishment decisions by designing models for safety stock, reorder points, and service levels to optimize in-stock rates and inventory investment.
• Partner with Finance and Merchandising stakeholders to align on assumptions, improve forecast accuracy, and deliver insights that directly inform planning, budgeting, and assortment decisions.
• Define and execute analytical approaches for complex problems, including pricing and price elasticity, labor optimization, and scenario planning where standard solutions may not exist.
• Build, validate, and deploy predictive models using best practices in feature engineering, back testing, and model evaluation, ensuring accuracy, scalability, and interpretability.
• Design and analyze experiments or quasi experiments to measure impact and support data-driven decision-making.
• Develop production-ready data products and models and collaborate with data engineering to integrate outputs into dashboards, planning tools, and operational workflows.
• Perform exploratory and ad hoc analysis on large transactional datasets to uncover insights, diagnose issues, and support strategic initiatives.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 501-1,000 employees