Staff Applied Scientist (LTV Modeling)

Join Faire, a leading online wholesale marketplace, as a Staff Applied Scientist on the Discovery team. In this role, you will be responsible for measuring and optimizing the long-term value of discovery impressions, helping retailers find brands and products they love, and strengthening existing promising relationships. You will create the initial LTV framework, lead the implementation of the LTV model, and continuously improve its capabilities and accuracy. This is a unique opportunity to define a measurement problem from first principles and make a significant impact on the marketplace.

• Responsable de la mesure et de l'optimisation de la valeur à long terme d'une impression de découverte.
• Création du cadre initial de la valeur à long terme (LTV) : formulation et priorisation des hypothèses.
• Mise en œuvre de la version 0 du modèle LTV dans une expérience de classement à long terme.

• Baseline knowledge of search and recommendation systems on e-commerce or marketplace platforms
• Strong experimentation design skills, especially long-horizon experiments — surrogate/proxy metrics and variance reduction for sparse, delayed outcomes
• Excellent communication skills and the ability to work in a highly cross-functional team
• Deep causal inference expertise — quasi-experimental methods, rigorous confounder control, and healthy skepticism of analytical results
• An excitement and willingness to learn new tools and techniques
• Strong statistical analysis and data engineering skills — SQL/ETL and data transformation at scale
• 5+ years applying ML and statistical modeling to real business problems, shipping to production
• Deep learning, machine learning, or learning-to-rank techniques
• LTV / lifetime-value optimization on a two-sided marketplace, e-commerce platform, or other recommendation systems
• PhD in CS, Stats, Economics, OR, or a related STEM field