Staff Applied Scientist, LTV Modeling

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.