Senior Applied Scientist, Parts Intelligence & Inventory Optimization
Core
Build decision models, optimization routines, and AI-powered tools for the Parts Agent to answer inventory questions like reorder points, stock optimization, and stockout risk.
Role type
Senior Applied Scientist (Inventory Optimization & ML)
Builds
Parts Agent intelligence layer for enterprise maintenance teams
Domain
Supply Chain / 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), Operations Research, Industrial Engineering, Statistics
Preferred skills
MRO inventory domain experience, learning-augmented optimization, tech-lead experience
Technologies
Python, APIs, async, observability tools
Responsibilities
Own and evolve optimization/ML models for reorder prediction and stock balancing; Design inventory intelligence features like vendor lead time modeling and safety stock; Build APIs exposing models to GenAI agent workflows; Partner with product/design to translate real-world problems into tractable models; Iterate with users to refine model recommendations; Contribute to Python service performance and reliability
Seniority
Senior, hands-on IC
