Senior Applied Scientist, Parts Intelligence, Inventory Optimization
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Job Description
Senior Applied Scientist developing optimizing and ML models for inventory management at MaintainX. Shaping decision processes and interacting closely with product and design teams.
Responsibilities:
- 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.
Requirements:
- 5+ years of professional software engineering or data science experience, with significant time spent 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: APIs, async, testing, profiling, observability. You can own a production service end-to-end.
- Academic grounding in Operations Research, Industrial Engineering, Supply Chain, Statistics, or a related quantitative field; strong undergraduate foundation at minimum.
- 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.
- Product mindset and delivery orientation: you ship, you measure, you iterate. You care about the operator outcome, not just the metric.
- Comfort with ambiguity. You can co-design the data model and feature schema with the team rather than waiting for a clean spec.
- Familiarity with GenAI tooling (LLM tool calling, structured output, prompt design for constrained generation) is expected.
Benefits:
- Competitive salary and meaningful equity opportunities.
- Healthcare, dental, and vision coverage.
- 401(k) / RRSP enrollment program.
- Take what you need PTO.
- A work culture where you'll work alongside folks across the globe that reflect the MaintainX values: Smart Humble Optimists. We believe in meritocracy, where ideas and effort are publicly celebrated.
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