Retail Media Network Strategy Consultant
Open a video or resource link, watch it, then check its box to track your progress and unlock what comes next.
- Connect retail-media strategy to a trusted product-data foundation
- Turn fragmented catalog signals into validated, agent-ready product knowledge
- Activate authoritative product knowledge across customer, agent and media experiences
Benchmark: Walmart Connect Agentic Ads + Amazon Selection & Catalog Systems + Walmart Sparky / agentic shopping (current Sep 2026) Market refresh: retail media and commerce AI; catalog/PIM source-of-truth strategy; taxonomy, attributes and product-data quality; agentic enrichment with provenance, human validation and PIM write-back; knowledge graphs/GraphRAG; semantic search, recommendations and conversational shopping; structured-data and answer-engine discoverability; APIs, governance, experimentation and closed-loop measurement.
Search Live Openings
We don’t have a direct job-board feed matched to this one yet, so here are real, live US job searches for “Retail Media Network Strategy Consultant” instead.
Connect retail-media strategy to a trusted product-data foundation
Retail media, commerce AI + closed-loop customer/advertiser value
Coverage: Retail Media / Ads & Monetization / Customer Journey / Incrementality
Product-data foundation: PIM, taxonomy, attributes + quality
Coverage: PIM / Product Taxonomy / Product Attributes / Data Quality / Commerce
Turn fragmented catalog signals into validated, agent-ready product knowledge
Agentic enrichment, source orchestration + validated PIM write-back
Coverage: Agentic AI / Data Pipelines / PIM Integration / Human-in-the-Loop
Knowledge graphs, GraphRAG + trusted product relationships
Coverage: Knowledge Graphs / GraphRAG / Semantic Modeling / AI Grounding
Activate authoritative product knowledge across customer, agent and media experiences
Semantic search, recommendations + conversational product discovery
Coverage: Search & Retrieval / Recommenders / Personalization / Conversational Commerce
Answer-engine discoverability + reusable commerce knowledge architecture
Coverage: Structured Product Data / Merchant Feeds / APIs / Platform Strategy / Measurement
Role-Readiness Proof / Capstone
Design a retail product-knowledge and retail-media foundation for a large retailer. Ingest demand, search, answer-engine and conversational-AI signals to identify catalog gaps; define canonical product entities, taxonomy and quality rules; build an agentic enrichment workflow that proposes attributes/content from trusted evidence with confidence and provenance; require human validation for high-risk changes; write approved updates back to the PIM and downstream APIs; project validated product knowledge into a graph for merchandising, discovery and supply-chain agents; expose retrieval for website, mobile app, search, recommendations, PDPs, conversational shopping and external answer engines; run the query 'waterproof hiking boots for wide feet under $150' end to end; then define experiments and closed-loop metrics across discovery quality, conversion, media performance, data quality and operating cost, plus an extension plan for another business domain.
Proof Rubric
- Prove authoritative-source and ownership boundaries
- measurable completeness, accuracy and provenance
- safe agent permissions and human-approval gates
- correct PIM write-back and downstream consistency
- graph/semantic-model quality
- grounded retrieval that satisfies constraint-rich customer intent rather than keyword matching
- structured-data/feed discoverability
- search, recommendation and conversational-AI evaluation
- retail-media incrementality and closed-loop measurement without degrading customer trust
- observability, rollback and cost controls
- and a reusable architecture and roadmap that can expand beyond product data