Product Manager, Product & Merchandise Analytics

Vuori, Inc•Carlsbad, CA

4h•$137,300 - $176,610•Onsite

About The Position

Vuori is seeking a Product Manager for its Product & Merchandising Analytics pod. The role supports Product Development, Merchandising, Merchandise Planning, Wholesale, and Supply Chain in partnership with ERP and business systems teams. Working with the Director, the Product Manager shapes the analytics roadmap and independently leads initiatives from discovery through delivery and adoption. The work supports decisions across the product lifecycle, including product cost and margin, assortment strategy, planning, distribution, and inventory management. The Product Manager works with business leaders, analytics leads, engineers, and reporting developers to identify valuable problems and deliver appropriate data and tools. The role requires turning open-ended business questions into scoped solutions, navigating competing priorities, communicating business tradeoffs to senior leaders, and defining metrics and data requirements with delivery teams. Responsibilities also include maintaining reliable data and consistent definitions, coordinating validation and rollout, assessing outcomes after launch, managing cross-team dependencies, and identifying appropriate uses for AI-enabled insights and workflow automation.

Requirements

5+ years of experience in product management, analytics, business intelligence, or a senior analyst role.
Exceptional stakeholder management skills, including leading discussions with senior leaders, gathering requirements across functions, and aligning competing needs.
Very strong written and verbal communication skills, with the ability to translate between business and technical audiences.
Strong understanding of retail and product lifecycle analytics, including assortment, seasonality, product cost, margin, sell-through, inventory productivity, and demand and supply planning.
Hands-on experience with a data semantic layer or modern BI platform, such as MicroStrategy, Looker, Power BI semantic models, dbt Semantic Layer, Cube, or equivalent.
Ability to translate business processes into data and analytics requirements, including metric design and definition, data grain, hierarchies, relationships, data quality, edge cases, and versioning.
Experience building analytics products for leadership and operational users, and understanding how to drive adoption and turn data into decisions.
Awareness of AI applications in analytics, including automated insights, semantic search, natural language to SQL, and workflow automation, and willingness to prototype, experiment, and use AI tools in personal workflows.

Nice To Haves

Experience supporting product, merchandising, planning, wholesale, or supply chain functions.
Experience in direct-to-consumer (DTC), apparel, retail, or branded consumer goods.

Responsibilities

Partner with the Director to shape the Product & Merchandising Analytics roadmap and independently manage initiatives, stakeholder relationships, and day-to-day product decisions.
Lead discovery to understand business decisions and friction, challenge assumptions, and determine whether reporting, better data, automation, or workflow changes are appropriate.
Prioritize requests based on business impact, urgency, effort, and dependencies; recommend what to pursue, defer, or stop and align stakeholders when needs compete.
Translate business questions and operational workflows into requirements, metric definitions, data needs, acceptance criteria, scope, and success measures.
Own the product backlog and work with engineers and reporting developers on sprint planning, scope decisions, and issue resolution.
Partner with business owners and engineering to connect product, assortment, planning, sales, inventory, purchase order, and ERP data; resolve conflicting definitions, validate results, and communicate data limitations.
Coordinate business testing, resolve issues before release, and ensure teams understand how to use solutions in existing workflows.
Assess post-launch impact using adoption, business feedback, and agreed success measures; guide improvements and retire reporting that is no longer needed.
Coordinate shared metrics, data definitions, and delivery dependencies with other analytics pods and business systems teams to avoid conflicting solutions and duplicate work.
Identify opportunities for AI-enabled insights and workflow automation, prototype and validate ideas, and partner with Engineering and DS&AI as appropriate.
Use approved AI tools to improve discovery, specifications, analysis, and prototyping.
Communicate priorities, progress, tradeoffs, and risks; make recommendations, surface decisions needing leadership input, and escalate issues before they block delivery.