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Salary: USD 220,000 - 302,500 / annual
Faire is a technology wholesale platform connecting independent retailers globally with suppliers. This Staff Product Manager role owns Faire's data and ML/AI platforms as products, treating them with the same rigor as customer-facing features.
You will define user needs and measurable outcomes for internal technical users (ML engineers, data scientists, product teams), establish a roadmap that unblocks platform adoption, and drive quality, safety, and adoption metrics. Key responsibilities include:
- Own the end-to-end ML platform journey: run discovery with ML engineers and data scientists, prioritize investments in training, deployment, observability, and model registry, and drive adoption metrics that shape engineering priorities.
- Establish trust in Faire's core data through SLAs, pipeline health, data contracts, and clear dataset ownership. Make the cross-functional case for quality investments competing with feature work.
- Improve metadata quality, lineage, and discovery so teams can find and trust data. Establish access controls and compliance processes for data deletion, access management, and auditability.
- Accelerate safe AI adoption by identifying high-value internal use cases (AI-assisted analytics, natural-language exploration) and driving them to production.
- Build abstractions and shared services so product teams without ML expertise can ship AI features safely. Define evaluation frameworks covering pre-production checks, production observability, and trace-level analysis.
- Develop data products for internal users: fact and dimension tables, semantic layers, reusable data products with strong discoverability and developer experience.
- Establish a durable product-platform operating model with published roadmap, clear intake process, adoption tracking, and platform cost/efficiency as health metrics.
Required: 8+ years product management experience with ownership of complex technical products at Staff or Lead scope. Technical fluency across data, ML, and AI platforms with credible engagement on architecture tradeoffs. Experience shipping platform products for internal technical users. Background in marketplace or data-rich products where data and AI drive algorithmic choices (search, recommendations, personalization). Strong cross-functional influence and strategic prioritization without positional authority.
