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Principal Product Manager - Commerce AI Platform

Microsoft · microsoft.com
Mountain ViewLead / Manager10+ yrs
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The job in short

Own catalog API strategy, roadmap and product data quality for commerce AI platform.

LLMAgenticMarketingLegalProduct management

Details

Found
5 d ago · posted 2026-11-03
Location
Mountain View, CA, US

Full description

Catalog API strategy: Own the vision, strategy, and roadmap for the catalog APIs (product, offer, and Product Graph) that power Copilot, Bing, Edge, and external AI partners, including schemas, versioning, documentation, and the coverage, freshness, latency, and availability bar each consumer needs. Trusted exposure: Define what the catalog exposes and to whom, including data-rights boundaries, merchant controls and exclusions, attribution, and Responsible AI, privacy, and security requirements, in partnership with Legal, Privacy, and Security. Partner and developer readiness: Drive onboarding, evaluation criteria, usage and quality telemetry, and the staged path from offline evaluation to limited pilot to production traffic for internal surfaces and external partners. Catalog gaps: Partner with engineering and applied science leaders to turn catalog coverage and quality gaps into a prioritized, measurable roadmap across demand coverage, product identity and clustering, attribute and metadata depth, taxonomy, price and availability accuracy, and freshness. Supply and ingestion: Own the import and ingestion roadmap with engineering, including merchant feeds and commerce platform connectors, so new supply lands in the catalog complete, accurate, and fresh. Metrics and evaluation: Define success metrics and evaluation frameworks for catalog and API quality (for example coverage, accuracy, freshness, defect rate, and downstream answer quality), make sure the telemetry exists, and use the results to drive investment and tradeoff decisions. Customer and market insight: Act as the subject matter expert on catalog and product data across divisional boundaries. Synthesize telemetry, evaluations, partner and merchant feedback, and industry trends in product data and agentic commerce into roadmap bets. Strategy and alignment: Write the strategy documents, PRDs, and release criteria for catalog APIs and gap programs, review them with senior leaders, and align stakeholders across MAI and partner teams on priorities and tradeoffs. Independent execution: Lead a large, senior engineering team as an individual contributor. Prioritize the backlog, land difficult tradeoffs, identify capabilities to deprecate, and lead transitions without disrupting consumers. Go-to-market: Partner with business development, product marketing, and partner teams on adoption, retention, and monetization of catalog capabilities. Embody our culture and values. Bachelor's Degree AND 10+ years experience in product/service/program management or software development Bachelor's Degree AND 15+ years experience in product/service/program management or software development OR equivalent experience. 6+ years experience taking a product, feature, or experience to market (e.g., design, addressing product market fit, and launch, internal tool/framework). 8+ years experience improving product metrics for a product, feature, or experience in a market (e.g., growing customer base, expanding customer usage, avoiding customer churn). 8+ years experience disrupting a market for a product, feature, or experience (e.g., competitive disruption, taking the place of an established competing product). 5+ years experience in product catalogs, product data platforms, marketplaces, or commerce search (for example product identity and matching, taxonomy and attribute enrichment, merchant feed ingestion, or price and availability data). Experience shipping and scaling externally consumed APIs or data platforms, including schema design, versioning, documentation, service levels, and partner onboarding. Experience defining quality metrics and evaluations for data or AI systems (for example coverage, precision and recall, freshness, and factuality) and using them to set engineering priorities. Technical fluency to engage deeply with engineering and applied science on data pipelines, ML-based matching and enrichment, and LLM grounding. Track record of leading large, senior engineering teams as an individual contributor and influencing stakeholders across organizational boundaries and with external partners.

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