Staff Applied Scientist, LTV Modeling

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. You will define a measurement problem from first principles, build the LTV framework, design 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 love, and how it strengthens existing relationships.
• Create the initial LTV framework: form and prioritize hypotheses regarding drivers of long-term relationship value and short- vs. long-term tradeoffs, defining explicit and testable assumptions alongside an experimentation roadmap.
• Lead the implementation of v0 of the LTV model into a long-running ranking experiment, establishing north star metrics and guardrails with defined readout cadences and course-correction plans.
• Deliver the long-term surrogate metric predictive of long-term value, accounting for confounding factors, measurement uncertainties, and marketplace dynamics.
• Own the LTV model tech stack and operating standards, continuously improving model capabilities and accuracy across search, reorder, and ads.

Qualifications

Required

• 5+ years of experience applying ML and statistical modeling to real business problems and shipping to production.
• Deep causal inference expertise, including quasi-experimental methods, rigorous confounder control, and critical analysis of analytical results.
• Strong experimentation design skills, especially long-horizon experiments, surrogate/proxy metrics, and variance reduction for sparse, delayed outcomes.
• Baseline knowledge of search and recommendation systems on e-commerce or marketplace platforms.
• Strong statistical analysis and data engineering skills (SQL/ETL and data transformation at scale).
• Excitement and willingness to learn new tools and techniques.
• Excellent communication skills and ability to collaborate across cross-functional teams.

Preferred

• PhD in Computer Science, Statistics, Economics, Operations Research, or a related STEM field.
• Experience with LTV / lifetime-value optimization on two-sided marketplaces, e-commerce platforms, or recommendation systems.
• Experience with deep learning, machine learning, or learning-to-rank techniques.