Senior Applied Scientist (Parts Intelligence & Inventory Optimization)

Join MaintainX as a Senior Applied Scientist, where you'll be responsible for developing the intelligence layer behind our Parts Agent. This high-ownership role involves building decision models, optimization routines, and AI-powered tools to optimize inventory management. You'll collaborate closely with product and design teams, iterate with real users, and contribute to the overall performance and reliability of the inventory intelligence runtime.

Tech-lead experience or interest in growing into a tech-lead role on this team

Domain experience in MRO (Maintenance, Repair & Operations) inventory, spare parts management, field service logistics, or manufacturing supply chains

USD 131,400 - 236,000

Responsibilities

• Posséder et faire évoluer les modèles d'optimisation et d'apprentissage automatique qui alimentent les capacités de l'agent de pièces.
• Concevoir et mettre en œuvre une intelligence d'inventaire de plus en plus sophistiquée, y compris la modélisation des délais de livraison des fournisseurs et l'alerte proactive aux ruptures de stock.
• Construire et maintenir des API et des outils qui exposent ces modèles aux flux de travail de l'agent GenAI, permettant à l'agent de pièces de prendre des mesures fondées et explicables.
• 5+ years of professional software engineering or data science experience, with significant time spent on optimization, forecasting, or ML systems shipped to real users

Requirements

• Academic grounding in Operations Research, Industrial Engineering, Supply Chain, Statistics, or a related quantitative field; strong undergraduate foundation at minimum
• Product mindset and delivery orientation: you ship, you measure, you iterate. You care about the operator outcome, not just the metric
• 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: APIs, async, testing, profiling, observability. You can own a production service end-to-end
• Track record of iterating data-driven systems with real users — you've felt what happens when a model recommendation gets rejected and you've redesigned the approach in response
• Familiarity with GenAI tooling (LLM tool calling, structured output, prompt design for constrained generation) is expected
• Comfort with ambiguity. You can co-design the data model and feature schema with the team rather than waiting for a clean spec

Preferred

• Experience at a known product company shipping inventory management, supply chain, or procurement optimization at scale
• Exposure to learning-augmented optimization — using historical purchasing or consumption data to estimate lead times, priors, or constraint weights