Opportunity at
FaireStaff Applied Scientist, LTV Modeling
Faire operates a global technology wholesale marketplace that connects independent retailers with brands using data, machine learning, and digital tools to support local commerce. The Discovery…
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About This Role
Faire operates a global technology wholesale marketplace that connects independent retailers with brands using data, machine learning, and digital tools to support local commerce. The Discovery team is currently seeking a Staff Applied Scientist focused on LTV Modeling to work out of the New York office. This position is a hybrid role requiring attendance in the office three days per week, specifically Tuesdays, Thursdays, and a flexible third day chosen…
Job Description
Faire operates a global technology wholesale marketplace that connects independent retailers with brands using data, machine learning, and digital tools to support local commerce. The Discovery team is currently seeking a Staff Applied Scientist focused on LTV Modeling to work out of the New York office. This position is a hybrid role requiring attendance in the office three days per week, specifically Tuesdays, Thursdays, and a flexible third day chosen by the employee.
In this role, the incumbent will take ownership of measuring and optimizing the long-term value generated by discovery impressions on the platform. Responsibilities include building the foundational lifetime value framework, creating experimentation roadmaps, implementing initial models into ranking experiments, and delivering long-term surrogate metrics. The ideal candidate brings extensive background in applied machine learning, statistical modeling, and causal inference within e-commerce or marketplace settings. Team members benefit from competitive compensation packages that include equity, comprehensive health benefits, enterprise AI tooling, and flexibility including up to four weeks of remote work per year.
Responsibilities
- Manage the measurement and optimization of long-term value derived from discovery impressions.
- Formulate the initial lifetime value framework and prioritize hypotheses regarding long-term relationship value.
- Deploy the first version of the lifetime value model into long-running ranking experiments with defined metrics and guardrails.
- Develop long-term surrogate metrics that predict long-term value while accounting for market dynamics and confounding variables.
- Maintain the lifetime value model technology stack and operating standards for use across search, reorder, and ads.
Requirements
- At least 5 years of professional experience applying machine learning and statistical modeling to business problems and deploying them to production.
- Deep expertise in causal inference, including quasi-experimental methods and confounder control.
- Strong background in experimentation design, particularly for long-horizon experiments involving sparse or delayed outcomes.
- Foundational understanding of search and recommendation systems within e-commerce or marketplace platforms.
- Advanced data engineering and statistical analysis capabilities utilizing SQL and ETL processes at scale.
Qualifications
- Possession of a PhD in Computer Science, Statistics, Economics, Operations Research, or a related STEM field.
- Prior experience with lifetime value optimization in two-sided marketplaces, recommendation engines, or e-commerce environments.
- Familiarity with deep learning, machine learning, or learning-to-rank methodologies.
Core Skills
Benefits
- Base salary range between $246,500 and $339,000 per year
- Equity eligibility
- Comprehensive benefits package
- Access to enterprise AI tools
- Flexibility to work remotely up to 4 weeks per year
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
What is the salary range for the Staff Applied Scientist, LTV Modeling position?
The base pay range for this role in New York is $246,500 to $339,000 per year, and the position is also eligible for equity and benefits.
Is this position remote or on-site?
This is a hybrid role based in New York. Employees work in the office three days per week on Tuesdays, Thursdays, and a third flexible day of their choosing, with the option to work remotely for up to four weeks per year.
What level of experience is required for this role?
Candidates must have at least 5 years of experience applying machine learning and statistical modeling to real business problems and shipping solutions to production.
Sample Interview Questions
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