Applied Scientist

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 the Role

As a Staff Applied Scientist on the Discovery team, you'll own how Faire measures and optimizes the long-term value of a discovery impression — one of the highest-leverage open problems on our marketplace. Our rankers today optimize for order conversion, helping retailers find brands and products they love. But we know our ranking algorithms can do more: helping retailers find not just products they love, but brands they can build long-lasting, successful partnerships with.

Reordering is one clear signal of this — successful brand-retailer relationships compound into substantial reorder volume over time — but not every discovery order evolves into a lasting partnership. Identifying the ones that will compound, and helping them grow, matters enormously for our community.

This is a rare opportunity to define a measurement problem from first principles. You'll build the LTV framework, design the experiments that validate it, and turn the result into a shared signal that all discovery algorithms can act on.

What You'll Do

• Own how we measure and optimize the long-term value of a discovery impression — how it contributes to the discovery of new brands retailers might love, and how it strengthens existing promising relationships so they compound.
• Create the initial LTV framework: form and prioritize hypotheses about what drives long-term relationship value and the key short- vs. long-term tradeoffs, with assumptions made explicit and testable, and lay out the experimentation roadmap to validate and refine it.
• Lead the implementation of v0 of the LTV model into a long-running ranking experiment, setting north star metrics as well as guardrails to maximize organizational learning, with a defined readout cadence and course-correction plan.
• Deliver the long-term surrogate metric — a near-term readout predictive of long-term value — accounting for confounding factors and inherent uncertainties in measurement and marketplace dynamics.
• Own the LTV model tech stack and operating standards, continuously improving the capabilities and accuracy of the model as it becomes consumable across search, reorder, and ads.