Staff Applied Scientist, LTV Modeling
Core
Define and build a Long-Term Value (LTV) framework to measure and optimize the long-term value of discovery impressions for independent retailers on a wholesale marketplace.
Role type
Staff Applied Scientist (LTV Modeling)
Builds
LTV framework, long-running ranking experiments, and surrogate metrics for discovery algorithms
Domain
E-commerce marketplace, recommendation systems, causal inference
Deliverable
production ML models
Required skills
causal inference, experimentation design, statistical modeling, data engineering, search/recommendation systems knowledge
Preferred skills
LTV optimization on marketplaces, deep learning, learning-to-rank
Technologies
SQL, ETL
Responsibilities
Form and prioritize hypotheses for long-term relationship value, lead implementation of v0 LTV model into ranking experiments, deliver long-term surrogate metrics, own the LTV model tech stack and operating standards
Seniority
Staff, hands-on IC with strategic ownership
