We are seeking a Product Manager to lead AI, Data, and Data Products for Merchandising, translating business opportunities into scalable capabilities that improve merchandising decisions and productivity.
This role will partner across Merchandising, Data & AI, Engineering, and Technology to build trusted, reusable data products, identify high-value AI use cases, and drive solutions from concept through adoption and measurable value realization.
The ideal candidate combines strong product management and retail merchandising experience with practical knowledge of data products, AI/ML, GenAI, analytics, and automation.
Key Responsibilities
- Own the product strategy, roadmap, and backlog for AI, Data, and Data Products across Merchandising.
- Define and deliver reusable, governed data products that provide trusted merchandising data for analytics, AI, operational applications, and decision-making.
- Partner with merchants to identify and prioritize opportunities across category management, assortment, pricing & promotion, vendor performance, inventory, and merchant productivity.
- Translate business needs into data product requirements, AI use cases, user stories, and measurable outcomes.
- Partner with Data Engineering, Data Science, Architecture, and Engineering to deliver scalable data pipelines, semantic/data models, APIs, analytics, and AI capabilities.
- Establish clear data ownership, quality, lineage, governance, SLAs, and adoption metrics for merchandising data products.
- Drive AI capabilities from discovery and proof-of-concept through production, adoption, and scale.
- Leverage GenAI, predictive analytics, optimization, intelligent agents, and workflow automation to transform merchant workflows.
- Manage dependencies and integrations across merchandising platforms and the enterprise data ecosystem.
- Define KPIs and business cases tied to sales, margin, inventory productivity, decision speed, data adoption, and manual-effort reduction.
- Lead cross-functional prioritization and executive product reviews across Merchandising, Data, and Technology.