Staff Product Manager, Data & ML Platform

Faire•New York City, NY

1d•Hybrid

About The Position

We're looking for a Staff Product Manager to treat Faire's data and ML platforms as products: define clear user needs, measurable outcomes, and a roadmap that accelerates every team building on these foundations. You will make data trustworthy by default and make ML capabilities easy to adopt and operate in production. When the data Faire runs on is high-quality and easy to use, every team that depends on it makes better decisions and ships better products. ML powers Faire's marketplace, from search and recommendations to personalization. Shared data sits underneath nearly every product decision we make. This role sits at that foundation: you will run structured discovery with the engineers and scientists who build on these platforms, prioritize the investments that unblock them, and hold the bar on quality, reliability, and adoption.

Requirements

8+ years of product management experience, with ownership of complex technical products at Staff or Lead scope.
Technical fluency across data and ML platforms: you have worked directly with data and ML systems, can engage credibly with engineering on architecture tradeoffs, understand modern ML in production (model training and deployment, feature pipelines, evaluation), and translate technical complexity into clear strategic choices.
Experience shipping platform products for internal technical users, including discovery, success metrics, adoption, and operational discipline.
Experience shipping in a marketplace or other data-rich product context where the product depends on data feeding algorithmic choices such as search, recommendations, or personalization.
You understand how upstream data and ML decisions translate into conversion, relevance, and retention.
Cross-functional influence and strategic prioritization: credibility with senior engineers and data scientists, the ability to negotiate without positional authority, ruthless sequencing, and clarity about what the team is choosing not to do.

Responsibilities

Own the path from ML idea to production: run structured discovery with ML engineers and data scientists, prioritize platform investments across training, deployment, observability, and model registry, and drive adoption so usage signals shape engineering priorities.
Measure ML platform success through user outcomes, including time saved, models shipped, and support load, rather than process metrics alone.
Increase trust in Faire's core data by defining SLAs, driving pipeline health on datasets the business depends on, establishing data contracts and clear dataset ownership, and making the cross-functional case for quality investments that compete with feature work.
Improve metadata quality, lineage, and discovery so teams can find, trust, and trace data, and establish access controls and durable compliance processes for data deletion, access management, and auditability.
Build the data products internal users actually need through embedded discovery: fact and dimension tables, semantic layers, reusable data products, and first-class discoverability and developer experience.
Establish a durable product-platform operating model with a published roadmap the company can plan against, a clear intake process, adoption tracking across usage, retention, and satisfaction, and platform cost and efficiency as a health metric.