Senior Digital Commerce Strategist – Architect

Responsibilities

• Partner with product to shape store entry and way finding—how customers land, orient, and navigate toward the right equipment for them.
• Design and continuously improve the equipment experience: how products are presented, compared, configured, and contextualized across the journey.
• Own the underlying commerce data model and taxonomy product attributes, content structures, navigation logic, and the relationships between them with an eye toward downstream usability for both humans and AI systems.
• Translate business goals, customer research, and behavioral data into clear strategic recommendations and roadmaps for the product team.
• Define KPIs and measurement frameworks for commerce initiatives; design and run A/B tests to validate impact on conversion, AOV, and customer lifetime value.
• Build business cases and ROI models for proposed initiatives and present findings to senior leadership.
• Collaborate with engineering to define API contracts, content models, and integration patterns that scale.
• Support current AI execution across commerce—help evaluate opportunities, scope initiatives, and translate them into something the team can ship.
• Help structure commerce data, content, and taxonomy so it's AI-ready: clean, well-labeled, retrievable, and semantically meaningful.
• Contribute to feasibility assessments for AI initiatives, including model selection considerations, data requirements, and integration approach.
• Help define guardrails, evaluation criteria, and QA processes for AI-driven features before they reach customers.
• Stay current with the AI landscape at a working level enough to be a credible thought partner and to spot where AI genuinely helps versus where it doesn't.

• 5-7+ years of experience in digital commerce, e-commerce strategy, or a closely related field, with demonstrated hands‑on execution.
• Experience working on commerce surfaces such as store entry, navigation/way finding, PDPs, search, or merchandising, ideally with direct exposure to product information architecture and taxonomy.
• Working understanding of commerce data: product data models, content structures, attribution, and how analytics flows through the stack.
• Working knowledge of core AI/ML concepts (LLMs, embeddings, retrieval, recommendation systems) and hands‑on experience using prompting techniques and RAG patterns in a real work context.
• Familiarity with AI orchestration frameworks (Lang Chain, Llama Index, or similar) at a conceptual level.
• Comfort evaluating tradeoffs across AI approaches (prompting vs. retrieval vs. fine‑tuning) at a directional level, and translating those tradeoffs into business language.
• Strong project management skills and the ability to run multiple initiatives in parallel.
• Experience with taxonomy, PIM, or structured content systems.
• Strong data and analytics skills: GA4, Tableau, Looker, or similar BI tools, plus working SQL.
• Experience with A/B testing methodologies and experimentation frameworks.
• Familiarity with semantic search, vector databases (Pinecone, Weaviate, Chroma), or AI-assisted discovery.
• Exposure to UX or service design thinking, comfort collaborating closely with designers and researchers, and a feel for how IA, content, and interaction patterns shape the customer experience.