Senior Applied Scientist, Parts Intelligence, Inventory Optimization

Jobtailor; California (MO); On-site; USD 120,000 - 180,000; Full time

Job summary

Jobtailor is seeking a senior data/ML engineer to own optimization and forecasting models for Parts Agent. You will design inventory intelligence, lead model development, and expose capabilities via APIs for GenAI workflows.

You will collaborate with product and design to translate real-world inventory problems into scalable data-driven solutions. You will drive end-to-end production services, using historical data to enhance forecasts and stock management while ensuring performance, testing,

Qualifications

• 5+ years of professional software engineering or data science experience.
• Experience with optimization, forecasting, or ML systems shipped to real users.
• Fluency with 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 related 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.

Responsibilities

• Own and evolve optimization and ML models powering Parts Agent capabilities.
• Design and implement inventory intelligence like vendor lead time modeling and safety stock.
• Build and maintain APIs and tools exposing models to GenAI workflows.
• Partner with product and design to translate inventory problems into models.
• Iterate with users through design partnerships and pilots.
• Incorporate feedback from parts managers into models.
• Contribute to Python service performance, observability, testing, and reliability.
• Help integrate parts intelligence with the broader MaintainX product.
• Use historical usage data to continuously improve model inputs.

Skills

Optimization

Machine Learning

Demand Forecasting

Inventory Management

Python

API Development

Testing

Observability

Stochastic Programming

Product Mindset

Education

Undergraduate degree (minimum)

Academic grounding in OR/Industrial Eng/Supply Chain/Statistics

Tools

GenAI

LLM Tool Calling

Structured Output

Prompt Design

Job description

• 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

• 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

Core Competencies

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.

Hard Skills

• Optimization


• Machine Learning


• Demand Forecasting


• Inventory Management


• Python


• API Development


• Testing


• Profiling


• Observability


• Stochastic Programming

Soft Skills

• Product Mindset


• Delivery Orientation


• Comfort with Ambiguity


• Collaboration


• User-Centric Design

Industry Keywords

• Operations Research


• Industrial Engineering


• Supply Chain


• Statistics


• Data-Driven Systems

Tools & Technologies

• GenAI


• LLM Tool Calling


• Structured Output


• Prompt Design