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MaintainX, now part of Autodesk Operations Solutions, is seeking a Senior Applied Scientist to own the intelligence layer behind the Parts Agent—a strategic initiative on the Inventory & EAM roadmap. The Parts Agent sits atop a multi-layer parts data model (PartMaster, StockRecord, PhysicalInstance) and answers critical inventory questions: when to reorder, how to optimize stock levels across sites, which parts risk stockout, and how to reconcile messy supplier catalogs into a clean parts master.
You will own and evolve optimization and ML models powering Parts Agent capabilities, including reorder point prediction, economic order quantity, multi-site stock balancing, and demand forecasting. You'll design and implement sophisticated inventory intelligence features such as vendor lead time modeling, criticality-weighted safety stock, substitution graph traversal, and proactive stockout alerting. A key responsibility is building and maintaining APIs and tools that expose these models to GenAI agent workflows (tool calling, structured input/output), enabling the Parts Agent to take grounded, explainable actions.
This is a high-ownership role requiring close partnership with product and design to translate messy real-world inventory problems into tractable models. You'll iterate with real users via design partnerships and pilot deployments, taking feedback from parts managers and procurement teams seriously and reflecting it back into the model. You'll also contribute to the surrounding Python service, ensuring performance, observability, testing, and reliability of the inventory intelligence runtime.
Required: 5+ years of professional software engineering or data science experience with significant time on optimization, forecasting, or ML systems shipped to real users. 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 skills (APIs, async, testing, profiling, observability) and the ability to own a production service end-to-end. Academic grounding in Operations Research, Industrial Engineering, Supply Chain, Statistics, or related quantitative field; strong undergraduate foundation minimum. Track record of iterating data-driven systems with real users and a product mindset with delivery orientation. Comfort with ambiguity and familiarity with GenAI tooling (LLM tool calling, structured output, prompt design) is expected.
Nice to have: experience at a product company shipping inventory management, supply chain, or procurement optimization at scale; exposure to learning-augmented optimization; domain experience in MRO inventory, spare parts management, field service logistics, or manufacturing supply chains; tech-lead experience or interest in growing into a tech-lead role.
