About this role
About Faire
Faire is a technology wholesale platform built on the belief that the future is local. Independent retailers around the globe collectively represent a multi-hundred-billion-dollar wholesale market that has historically been fragmented and offline. At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe. Picture your favorite boutique in town — we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so businesses can grow and local communities can thrive.
We’re looking for smart, resourceful and passionate people to join us as we power the shop local movement. If you believe in community, come join ours.
About this role
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, and AI data agents are how that insight shows up in the day-to-day of building product. This role sits at that foundation: you will run structured discovery with the engineers, scientists, and product teams who build on these platforms, prioritize the investments that unblock them, and hold the bar on quality, reliability, and adoption. You will also establish the product-platform operating model — roadmap, intake, adoption, measurement — that Faire can extend as the broader product platform function scales.
What you’ll do
• 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.
• Scale the use of AI data agents: own the vision for agentic data insights Product, Design, and Engineering use to shape strategy; prioritize the data-engineering investments agents depend on; drive adoption across product development; and measure success through decisions accelerated and time saved on insight work, not demo metrics.
• 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 feat