Develop deep fluency in Love’s business, including product categories, customer behavior, pricing strategy, and financial performance. Take an active role in development, execution, and continuous improvement of Love’s category strategies, including pricing, promotion, and other performance optimization levers. Leverage data science and advanced analytics tools to forecast outcomes and recommend actions aligned to business objectives. Act as the primary analytics partner to Category Management, aligning enterprise analytical capabilities with category-specific business needs to design decision frameworks, guardrails, and strategies supporting pricing, promotion, and broader category optimization strategies that support both tactical execution and long-term category health. Lead pricing and promotion related analyses from problem framing through recommendation and execution, not just interpretation. Create and present clear, compelling narratives to guide Merchandising leaders and cross-functional partners. Measure and evaluate the impact of implemented decisions, clearly communicating results, learnings, and next steps. Participate in or lead competitive benchmarking and market intelligence initiatives to inform pricing and assortment decisions. Lead or contribute to system implementations and process improvements related to pricing, promotion, testing, and analytics. Support ongoing and future reporting, analytical, and insight needs across Merchandising and Operations. Experience and Qualifications: Bachelor’s degree required; Master’s degree preferred in a relevant quantitative or business field (e.g., mathematics, statistics, economics, finance, engineering, MIS, or MBA)1–3 years of experience preferred in retail, merchandising, pricing, marketing, or operations analytics. Exposure to retail pricing systems, promotional analysis, and pricing methodologies strongly preferred. Demonstrated experience troubleshooting analytical or business problems and driving issue resolution. Experience with Tableau, Power BI, SQL, Python, and R is a plus. Skills and Physical Demands: Strong analytical mindset with the ability to structure ambiguous problems and develop insight-driven solutions. Working understanding of statistical concepts and pricing fundamentals, including elasticity, promotional lift, cannibalization, and affinity analysis. Solid understanding of the retail business environment and how analytical decisions impact execution. Self-starter who takes ownership and thrives in a fast-moving, collaborative environment. Excellent written, verbal, and interpersonal communication skills, with the ability to influence without authority. Customer- and business-outcome-oriented, not just task-or analysis-oriented. Proven ability to work effectively in cross-functional, team-based settings#LI-Onsite