Senior Applied Scientist – Inventory Optimization

Job Description

Senior Applied Scientist, Parts Intelligence & Inventory Optimization | MaintainX

The Tone:
This is a full-time role at MaintainX. MaintainX builds a leading mobile-first work execution platform that helps industrial and frontline teams cut unplanned downtime and run better operations across millions of assets. This role is crucial for owning the intelligence layer behind the Parts Agent, a strategic component of the Inventory & EAM roadmap. You will develop decision models, optimization routines, and AI-powered tools to provide trustworthy answers to complex inventory questions for enterprise maintenance teams.

The TL;DR
• Role: Full Time
• Type: Full-time
• Location: Not specified

• Mission: To build the decision models, optimization routines, and AI-powered tools that answer hard inventory questions and optimize stock levels for enterprise maintenance teams.
• Tech Stack: Python (APIs, async, testing, profiling, observability), GenAI tooling (LLM tool calling, structured output, prompt design for constrained generation), Optimization paradigms (LP/MILP, stochastic programming, simulation), Demand forecasting models, ML models.

What You’ll Actually Do
• Model Ownership: Own and evolve the optimization and ML models that power Parts Agent capabilities like reorder point prediction, economic order quantity, multi-site stock balancing, and demand forecasting.
• Intelligence Design: Design and implement increasingly sophisticated inventory intelligence including vendor lead time modeling, criticality-weighted safety stock, substitution graph traversal, and proactive stockout alerting.
• API Development: Build and maintain APIs and tools that expose these models to GenAI agent workflows, enabling the Parts Agent to take grounded and explainable actions.
• Problem Translation: Partner with product management and design to translate messy real-world inventory problems into tractable models, ensuring “optimal” solutions meet operator needs.
• User Iteration: Iterate with real users through design partnerships and pilot deployments, incorporating feedback from parts managers and procurement teams to refine models.

The Must-Haves
• Background: Academic grounding in Operations Research, Industrial Engineering, Supply Chain, Statistics, or a related quantitative field, with a strong undergraduate foundation at minimum.
• Experience: 5+ years of professional software engineering or data science experience, including significant time spent on optimization, forecasting, or ML systems shipped to real users.
• Skills: Strong fluency with at least one optimization paradigm (LP/MILP, stochastic programming, simulation) and practical experience with demand forecasting or inventory management models; solid Python service engineering, including APIs, async, testing, profiling, and observability; familiarity with GenAI tooling (LLM tool calling, structured output, prompt design for constrained generation); a track record of iterating data-driven systems with real users; and a product mindset with a delivery orientation.
• Bonus: Experience at a known product company shipping inventory management, supply chain, or procurement optimization at scale; exposure to learning-augmented optimization; domain experience in MRO (Maintenance, Repair & Operations) inventory, spare parts management, field service logistics, or manufacturing supply chains; or tech-lead experience or interest in growing into a tech-lead role.