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MaintainX

Senior Applied Scientist, Parts Intelligence & Inventory Optimization

RemoteCanada only
Published
Role
Data Science
Experience
Senior
Employment
Full-time
Salary not disclosed
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Open to CA only. Set where you work from to check your eligibility.

No BS summary

Senior Applied Scientist with 5+ years of experience in software engineering or data science, focusing on optimization, forecasting, or ML systems. Must have strong Python service engineering skills and academic grounding in a quantitative field. Experience with GenAI tooling is expected.

Core skills

optimizationforecastingML systems

Required skills

PythonAPIsasynctestingprofilingobservabilityGenAI toolingLLM tool callingstructured outputprompt design for constrained generation

Optional skills

inventory managementsupply chainprocurement optimizationlearning-augmented optimizationMRO inventoryspare parts managementfield service logisticsmanufacturing supply chains

Required languages

English

What you'll do

  • 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.

What they require

  • 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.

MaintainX is an AI-powered maintenance and asset management platform for maintenance, repair, and operations teams.

Asset ManagementMid-size
Salary not disclosed