# Staff Applied Scientist, LTV Modeling

**Company**: Faire
**Location**: New York City, NY
**Work arrangement**: hybrid
**Experience**: staff
**Job type**: full-time
**Salary**: $246,500 to $339,000 per year
**Category**: Data Science
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/faire/jobs/8801834002?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_07bddb3e-fb2

## Description

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'll build the LTV framework, design experiments to validate it, and turn the result into a shared signal that all discovery algorithms can act on.

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.

Requirements:

- 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.

Benefits:

- Competitive pay, equity, and comprehensive benefits designed to support your life inside and outside of work.

- Move fast: You'll own meaningful problems that serve customers around the globe with the agency to move fast and see your results clearly.

- Equipped to scale: We invest in what matters, including the latest enterprise AI tools, to help you work smarter and get more out of every day.

- Best in class: Our team is full of sharp, kind, and generous colleagues who care about their craft and about helping you grow in yours.

- Belonging: We're intentional about building an environment where every Faire employee has equal access to opportunities, growth, and success.

## Skills

### Required
- Machine Learning
- Statistical Modeling
- Causal Inference
- Experimentation Design
- SQL
- ETL
- Data Transformation

### Nice to have
- PhD in CS, Stats, Economics, OR, or a related STEM field
- 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

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/faire/jobs/8801834002?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)