Staff Applied Scientist, LTV Modeling

Faire
New York, San Francisco
Workplace: HybridFull timeUSD 246,500 - 339,000 annuallyFunction: Research & Scientific (R&D)Experience: 5+ yearsSkills: ["Communication","Cross-functional collaboration","Healthy skepticism","Experimentation design","Continuous learning"]

Own measurement and optimization of the long-term value of a discovery impression on Faire’s Discovery team. Build the LTV framework from first principles, define hypotheses and an experimentation roadmap, and lead implementation of v0 into long-running ranking experiments with north-star metrics and guardrails. Deliver a long-term surrogate metric for delayed outcomes, and evolve the model and standards for use across search, reorder, and ads.

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Faire
Faire
4 hours ago

Staff Applied Scientist, LTV Modeling

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Last checked: 1 hour agoStatus: Live

Job Summary

Own measurement and optimization of the long-term value of a discovery impression on Faire’s Discovery team. Build the LTV framework from first principles, define hypotheses and an experimentation roadmap, and lead implementation of v0 into long-running ranking experiments with north-star metrics and guardrails. Deliver a long-term surrogate metric for delayed outcomes, and evolve the model and standards for use across search, reorder, and ads.
Location: New York, San Francisco
Workplace: Hybrid
Employment Type: Full time
Job Function: Research & Scientific (R&D)
Seniority: Mid level

Key Responsibilities

  • •Measure and optimize how a discovery impression drives long-term value, including brand discovery and compounding relationships.
  • •Create and prioritize the initial LTV framework with explicit, testable hypotheses and an experimentation roadmap.
  • •Lead implementation of the v0 LTV model into a long-running ranking experiment, setting north star metrics and guardrails for learning.
  • •Deliver a long-term surrogate metric that predicts long-term value while accounting for confounding and measurement uncertainty.
  • •Own the LTV model’s tech stack and operating standards, continuously improving accuracy and making it consumable across search, reorder, and ads.

Pay and Benefits

Salary: USD 246,500 - 339,000 annually
Equity and Bonus:Equity
Perks:Equity

Key Requirements

  • •5+ years applying ML and statistical modeling to real business problems with production shipping.
  • •Deep causal inference expertise, including quasi-experimental methods and confounder control.
  • •Strong experimentation design skills for long-horizon experiments using surrogate/proxy metrics and variance reduction.
  • •Baseline knowledge of search and recommendation systems on e-commerce or marketplace platforms.
  • •Strong statistical analysis and data engineering skills, including SQL/ETL and data transformation at scale.
Experience:5+ yearsMLE-commerceMarketplacesRecommendation systems
Skills:CommunicationCross-functional collaborationHealthy skepticismExperimentation designContinuous learning
Languages:English
Tech Stack:Machine LearningStatistical modelingCausal inferenceQuasi-experimental methodsSurrogate metricsVariance reductionSQLETLData transformationSearchRecommendation systemsDeep learningLearning-to-rank

Company Brief

Faire
Operates a wholesale marketplace connecting independent retailers with emerging and established brands, providing ordering, logistics, and payment solutions to help small businesses discover and stock unique products.
Industry: Online Marketplaces
Company Size: Enterprise (1,001+ employees)
Growth: Scaleup
Valuation: Unicorn (USD 1B+)
Headquarters: San Francisco, United States
Founded: 2017
WebsiteLinkedIn