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
• 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

Benefits

• Comprehensive healthcare: Including Health, Dental, Vision and Disability for all our locations.
• Time off: Paid time off, holidays and company-wide “Faire Fundays”.
• Parental leave: Generous parental and family leave, as well as fertility support benefits.
• Productivity support: Monthly stipends to help cover work from home connectivity needs.
• Annual learning grant: For personal and professional development, as well as unlimited access to training courses through LinkedIn Learning.
• Thoughtfully designed spaces: All of our offices have been designed with local in mind – from our architects to our coffee blends.
• Fitness and well-being benefits: Including monthly credit towards your wellness-related programmes.
• Mental health benefits: Including free access to Modern Health therapists and resources.
• Charitable matching: Faire will match up to £250 of your charity donations, every year.
• Career planning: Whether you want to grow as a leader, hone your craft or explore a new discipline, our career framework allows space for you to explore.- Baseline knowledge of search and recommendation systems on e-commerce or marketplace platforms
• Strong experimentation design skills, especially long-horizon experiments — surrogate/proxy metrics and variance reduction for sparse, delayed outcomes
• Excellent communication skills and the ability to work in a highly cross-functional team
• Deep causal inference expertise — quasi-experimental methods, rigorous confounder control, and healthy skepticism of analytical results
• An excitement and willingness to learn new tools and techniques
• Strong statistical analysis and data engineering skills — SQL/ETL and data transformation at scale
• 5+ years applying ML and statistical modeling to real business problems, shipping to production
• Deep learning, machine learning, or learning-to-rank techniques
• LTV / lifetime-value optimization on a two-sided marketplace, e-commerce platform, or other recommendation systems
• PhD in CS, Stats, Economics, OR, or a related STEM field