Senior Applied Scientist, Parts Intelligence & Inventory Optimization

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Senior From the posting source checked Jun 20, 2026

Work mode

Remote From the posting source checked Jun 20, 2026

In-office days

0 days From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Equity

Offered From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Senior Applied Scientist, Parts Intelligence & Inventory Optimization Canada (Remote) MaintainX is the world's leading AI-powered maintenance and asset management platform, serving 13,000+ customers including Duracell, Shell, Cintas, and Brenntag. We raised $150M in Series D funding led by Bessemer Venture Partners and Bain Capital Ventures, bringing our total funding to $254M. We were named to the Forbes 2025 Cloud 100, the definitive ranking of the top 100 private cloud companies in the world. We're growing fast and hiring the engineering talent to match. 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. 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.

Read the full description at job-boards.greenhouse.io. FewerJobs shows a preview and links to the original posting.