Senior Applied Scientist, Parts Intelligence, Inventory Optimization

MaintainX

Senior Applied Scientist building optimization, forecasting, and GenAI tools for MaintainX’s industrial maintenance platform. Improving parts inventory decisions for enterprise maintenance teams.

Posted 9/4/2026full-timeSan Francisco • California, New York, Texas, Washington • 🇺🇸 United StatesSenior💰 $131,400 - $236,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in optimization and machine learning models for inventory management, with strong proficiency in Python service engineering and API development. Capable of translating complex inventory challenges into actionable data-driven solutions while collaborating effectively with cross-functional teams.

Highest-signal resume keywords

Optimization Paradigm FluencyDemand Forecasting ExperiencePython Service EngineeringInventory Management ModelsGenAI Tooling Familiarity

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

OptimizationMachine LearningDemand ForecastingInventory ManagementPythonAPI DevelopmentTestingProfilingObservabilityStochastic Programming

Soft Skills

Product MindsetDelivery OrientationComfort with AmbiguityCollaborationUser-Centric Design

Tools & Technologies

GenAILLM Tool CallingStructured OutputPrompt Design

Industry Keywords

Operations ResearchIndustrial EngineeringSupply ChainStatisticsData-Driven Systems

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Own and evolve optimization and ML models powering Parts Agent capabilities, including reorder point prediction, economic order quantity, multi-site stock balancing, and demand forecasting
Design and implement inventory intelligence such as vendor lead time modeling, criticality-weighted safety stock, substitution graph traversal, and proactive stockout alerting
Build and maintain APIs and tools exposing models to GenAI agent workflows through tool calling and structured input/output
Partner with product management and design to translate real-world inventory problems into tractable models
Iterate with users through design partnerships and pilot deployments
Incorporate feedback from parts managers and procurement teams into models
Contribute to the Python service's performance, observability, testing, and reliability
Help integrate parts intelligence with the broader MaintainX product
Use historical usage and purchasing data to continuously improve model inputs

Requirements

What you’ll need

5+ years of professional software engineering or data science experience
Significant experience with optimization, forecasting, or ML systems shipped to real users
Fluency with at least one optimization paradigm: LP/MILP, stochastic programming, or simulation
Practical experience with demand forecasting or inventory management models
Solid Python service engineering, including APIs, async, testing, profiling, and observability
Ability to 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
Product mindset and delivery orientation
Comfort with ambiguity and co-designing data models and feature schemas
Familiarity with GenAI tooling, including LLM tool calling, structured output, and prompt design for constrained generation

Benefits

Comp & perks

Equity
Annual bonus
Health coverage
Retirement benefits
Leave benefits
Belonging and inclusion initiatives