Senior Applied Scientist (Parts Intelligence & Inventory Optimization)

• We’re looking for a Senior Applied Scientist to own the intelligence layer behind our Parts Agent — one of the most strategic bets on our Inventory & EAM roadmap
• The agent sits on top of a multi-layer parts data model (PartMaster, StockRecord, PhysicalInstance) and is responsible for answering hard inventory questions: when to reorder, how to optimize stock levels across sites, which parts are at risk of stockout, and how to reconcile messy supplier catalogs into a clean parts master
• Your focus will be building the decision models, optimization routines, and AI-powered tools that make those answers trustworthy enough for enterprise maintenance teams to act on
• This is a high-ownership role. You’ll shape the modeling approach, partner closely with product and design on what inventory managers actually need, and ship iteratively against feedback from real enterprise customers
• Own and evolve the optimization and ML models that power Parts Agent capabilities: reorder point prediction, economic order quantity, multi-site stock balancing, and demand forecasting
• Design and implement increasingly sophisticated inventory intelligence: vendor lead time modeling, criticality-weighted safety stock, substitution graph traversal, and proactive stockout alerting
• Build and maintain 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
• Partner with PM and design to translate messy real-world inventory problems into tractable models, and push back when “optimal” isn’t what operators actually want
• Iterate with real users via design partnerships and pilot deployments. Take feedback from parts managers and procurement teams seriously and reflect it back into the model
• Contribute to the surrounding Python service: performance, observability, testing, and reliability of the inventory intelligence runtime
• Help shape how parts intelligence integrates with the broader MaintainX product over time, including learning from historical usage and purchasing data to continuously improve model inputs

Benefits

• Flexible time off
• Comprehensive healthcare coverage
• Competitive salary and equity opportunities
• Training and development investments- 5+ years of professional software engineering or data science experience, with significant time spent on optimization, forecasting, or ML systems shipped to real users
• 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
• 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
• 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