Staff Applied Scientist, LTV Modeling & Discovery

Faire Wholesale Inc.; San Francisco (CA); Hybrid; USD 247,000 - 339,000; Full time

Benefits offered by this job

Equity

Benefits

Hybrid work

Job summary

Faire, a technology wholesale platform, seeks a Staff Applied Scientist to define and measure long-term value for discovery impressions. You will build an LTV framework, design experiments, and implement a v0 model within a sustained ranking experiment to improve long-term relationships and marketplace outcomes.

This role anchors on cross-functional collaboration across search, reorder, and ads, applying advanced ML, causal inference, and learning-to-rank techniques to drive scalable business

Qualifications

• 5+ years applying ML and statistical modeling to real business problems, shipping to production.
• Deep causal inference expertise — quasi-experimental methods, rigorous confounder control, and healthy skepticism 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.
• An excitement and willingness to learn new tools and techniques.
• Excellent communication skills and the ability to work in a highly cross-functional team.
• LTV / lifetime-value optimization on a two-sided marketplace, e-commerce platform, or other recommendation systems.
• Deep learning, machine learning, or learning-to-rank techniques.

Responsibilities

• 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.

Skills

ML & statistical modeling

Causal inference

Experimentation design

Search & recommendation systems

SQL/ETL & data transformation

Communication & cross-functional work

PhD in CS/Stats/Economics/OR

LTV concepts & learning-to-rank

Education

PhD in CS, Stats, Economics, OR, or related STEM field

Tools

Python

ML frameworks

Job description

Faire, a technology wholesale platform, seeks a Staff Applied Scientist to define and measure long-term value for discovery impressions. You will build an LTV framework, design experiments, and implement a v0 model within a sustained ranking experiment to improve long-term relationships and marketplace outcomes.

This role anchors on cross-functional collaboration across search, reorder, and ads, applying advanced ML, causal inference, and learning-to-rank techniques to drive scalable business