The Manager, Merchandising Analytics will lead the development of data-driven insights that optimize RaceTrac’s product assortment, pricing, inventory, and promotional strategies. This individual will partner closely with the various merchandising teams and will lead a team of analysts to gather, analyze, and present strategic business insights to drive decisions that enhance sales, profitability, and customer satisfaction.
What You'll Do:
Analyzes historical and current sales, inventory, margin, and pricing data to support product lifecycle decisions.
Builds and maintains merchandising dashboards and reporting tools for real-time visibility into product and category performance.
Develops and manages KPI frameworks to track success of merchandising strategies.
Communicates findings and recommendations clearly to both technical and non-technical stakeholders, including senior leadership.
Contributes to pricing optimization, promotions effectiveness analysis, and demand forecasting initiatives.
Executes high level ad hoc analysis in response to business requests involving pricing, profitability, and process adherence.
Utilizes SQL for data extraction, transformation, and analysis of operational performance metrics.
Consistently delivers quality services by monitoring progress and actively managing deliverables.
Demonstrates in-depth technical capabilities and professional knowledge.
Leads, mentors, and inspires a team, fostering a culture of data-driven decision-making, credibility, creativity, and continuous improvement.
Performs other duties and responsibilities as needed.
What We're Looking For:
Bachelor’s Degree from a 4-year College or University in Business Analytics, Finance, Business, or related field required.
5+ years of analytics experience required, preferably within the retail industry preferred.
1+ year of experience managing direct reports preferred.
Proficiency in PowerBI and SQL for data manipulation and analysis.
Previous experience managing projects and tracking progress required.
Previous Experience using Python and Databricks preferred.
Experience presenting complex analysis to non-technical stakeholders preferred.
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