Senior Applied Scientist, Parts Intelligence & Inventory Optimization
Core
Building the intelligence layer and decision models for a GenAI Parts Agent to optimize inventory, predict reorder points, and balance stock levels across sites for industrial maintenance teams.
Role type
Senior Applied Scientist (Operations Research & Inventory Optimization)
Builds
Parts Agent capabilities including reorder point prediction, economic order quantity, multi-site stock balancing, and demand forecasting models.
Domain
Industrial maintenance, supply chain, and inventory management (MRO)
Deliverable
production ML models
Required skills
Optimization paradigms (LP/MILP, stochastic programming), demand forecasting, Python service engineering, GenAI tooling (LLM tool calling, structured output), API design, observability
Preferred skills
Experience in inventory management or supply chain optimization at scale, MRO domain knowledge, learning-augmented optimization
Technologies
Python, GenAI frameworks, optimization libraries
Responsibilities
Own and evolve optimization and ML models for inventory intelligence; Design and implement vendor lead time modeling and safety stock strategies; Build APIs exposing models to GenAI agent workflows; Partner with product to translate real-world inventory problems into tractable models; Iterate with real users to refine model recommendations; Contribute to the reliability and performance of the Python service runtime
Seniority
Senior, hands-on IC
