Senior Digital Commerce Strategist & Architect

Caterpillar Caterpillar · Industrial · Westminster, CO +1

This role is for a Senior Digital Commerce Strategist & Architect at Caterpillar. The primary focus is on shaping the digital commerce experience, including store entry, navigation, and equipment presentation, while building data foundations for future AI-driven experiences. The role involves defining strategy, designing data models and taxonomies, translating business goals into roadmaps, and supporting current AI execution by evaluating opportunities and structuring data for AI readiness. While not a core AI development role, it requires a working understanding of AI/ML concepts, prompting, RAG, and evaluating AI approaches to be a credible thought partner.

What you'd actually do

  1. Partner with product to shape store entry and wayfinding how customers land, orient, and navigate toward the right equipment for them.
  2. Design and continuously improve the equipment experience: how products are presented, compared, configured, and contextualized across the journey.
  3. 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.
  4. Translate business goals, customer research, and behavioral data into clear strategic recommendations and roadmaps for the product team.
  5. Define KPIs and measurement frameworks for commerce initiatives; design and run A/B tests to validate impact on conversion, AOV, and customer lifetime value.

Skills

Required

  • Decision Making and Critical Thinking
  • Effective Communications
  • Software Change Request Management
  • Software Engineering
  • Software Problem Management
  • Software Product Business Knowledge
  • digital commerce
  • e-commerce strategy
  • product information architecture
  • taxonomy
  • PIM
  • structured content systems
  • commerce data models
  • content structures
  • attribution
  • analytics
  • project management

Nice to have

  • core AI/ML concepts (LLMs, embeddings, retrieval, recommendation systems)
  • hands-on experience using prompting techniques
  • RAG patterns
  • AI orchestration frameworks (LangChain, LlamaIndex, or similar) at a conceptual level
  • evaluating tradeoffs across AI approaches (prompting vs. retrieval vs. fine-tuning) at a directional level
  • translating those tradeoffs into business language

What the JD emphasized

  • AI-driven experiences
  • AI execution
  • AI-ready data
  • AI landscape
  • prompting
  • RAG patterns
  • AI orchestration frameworks