Who can apply: Only open to applicants in: New York.
Location as listed by the employer: “San Francisco; Austin; New York; Seattle”
Apply on Maintainx's site ↗
Opens the employer's official application page.
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About this role
Maintainx is hiring for a Senior Applied Scientist, Parts Intelligence & Inventory Optimization role (full-time). See the official posting for full details.
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.
Summarized from the employer's posting — check the full posting for every requirement.
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