Job description

Who We Are & Why Join Us Avathon is the leading Industrial AI autonomy platform, helping customers across heavy industries -- energy, mining, manufacturing, aerospace, defense, and logistics -- accelerate the journey toward autonomous operations. Our platform is built on a Computational Knowledge Graph foundation that contextualizes and connects operational data across siloed systems, bringing together time series, structured, unstructured, and machine vision data to power AI-driven applications in asset performance management, supply chain intelligence, visual AI, and global trade management. With capabilities spanning digital twins, normal behavior modeling, natural language processing, and computer vision, Avathon delivers real-time predictive intelligence and agentic decision-making at industrial scale. Cutting-Edge AI Innovation -- Join a team at the forefront of AI, developing groundbreaking solutions that shape the future. High-Growth Environment -- Thrive in a fast-scaling startup where agility, collaboration, and rapid professional growth are the norm. Meaningful Impact -- Work on AI-driven projects that drive real change across industries and improve lives. Learn more at: avathon.com We are hiring a Senior AI Scientist to build the AI and optimization models behind Planning on the Avathon platform: demand forecasting, supply planning, inventory optimization and S&OP decision support for customers in energy, mining, manufacturing and logistics. You will work with hard supply chain data, including demand history, ERP and MRP transactions and commercial forecasts across multi-echelon networks with long lead times and constrained capacity. Our models run on top of a Computational Knowledge Graph that connects parts, BOMs, suppliers, orders and assets, which gives you context most planning systems do not have. The work is equal parts applied research and production delivery. This role sits in Product Engineering, and you will take models from research to production with the Planning engineering team. You Will Forecasting Build demand and spare parts forecasting models using probabilistic, hierarchical and intermittent demand methods, including where history is short or volatile Use causal and external signals, and cross-domain signals from the knowledge graph such as asset health, logistics and procurement Define evaluation metrics, build backtesting pipelines and run controlled experiments to improve accuracy and bias Optimization Build supply planning and multi-echelon inventory optimization models with capacity constraints and variable lead times Formulate and solve MIP, LP and constraint programming problems for production scheduling, allocation and resource planning Build simulation and scenario analysis so planners and S&OP leaders can compare options and trade-offs Productization Own the path from research to production with ML engineering, with monitoring, retraining and clear explanations of results Work with AI agents so planners can ask why a plan changed and what to do next Mentor data scientists on OR and ML fundamentals Write up methods and results clearly for product, customers and leadership You'll Have PhD or MS in Operations Research, Industrial Engineering, Statistics, Computer Science, Applied Math or a related field 10+ years of industry experience in applied ML, optimization or supply chain planning systems Deep experience in at least two of: demand forecasting, supply and inventory optimization, production scheduling or S&OP analytics Hands-on Python with optimization solvers (Gurobi, CPLEX, OR-Tools or similar) and forecasting libraries (statsmodels, GluonTS or similar) Strong statistical modeling and good judgment on when simple methods beat complex ones Experience with real supply chain data, ideally at a planning vendor such as o9 Solutions, Blue Yonder, Kinaxis, Oracle or SAP, or at a large manufacturer Experience shipping models to production, not only notebooks Multi-echelon inventory, Bayesian methods for demand sensing, or publications in applied OR and ML are a plus Compensation Pay Range: $160k - $240k salary annually. Pay for this position is based on a number of factors including geographic location and may vary depending on job-related knowledge, skills, and experience. Location: This role is not remote. Candidates must be based in the Bay Area, CA and are expected to report to our Pleasanton office 5 days a week. Avathon is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, pregnancy, genetic information, disability, status as a protected veteran, or any other protected category under applicable federal, state, and local laws. Avathon is committed to providing reasonable accommodations throughout the recruiting process. If you need a reasonable accommodation, please contact us to discuss how we can assist you.

Requirements

Must-haves

Who We Are & Why Join Us
Learn more at: avathon.com
Build demand and spare parts forecasting models using probabilistic, hierarchical and intermittent demand methods, including where history is short or volatile
Use causal and external signals, and cross-domain signals from the knowledge graph such as asset health, logistics and procurement
Define evaluation metrics, build backtesting pipelines and run controlled experiments to improve accuracy and bias
Optimization
Build supply planning and multi-echelon inventory optimization models with capacity constraints and variable lead times
Formulate and solve MIP, LP and constraint programming problems for production scheduling, allocation and resource planning
Build simulation and scenario analysis so planners and S&OP leaders can compare options and trade-offs
Productization
Own the path from research to production with ML engineering, with monitoring, retraining and clear explanations of results
Work with AI agents so planners can ask why a plan changed and what to do next
Mentor data scientists on OR and ML fundamentals
Write up methods and results clearly for product, customers and leadership
PhD or MS in Operations Research, Industrial Engineering, Statistics, Computer Science, Applied Math or a related field
10+ years of industry experience in applied ML, optimization or supply chain planning systems
Deep experience in at least two of: demand forecasting, supply and inventory optimization, production scheduling or S&OP analytics
Hands-on Python with optimization solvers (Gurobi, CPLEX, OR-Tools or similar) and forecasting libraries (statsmodels, GluonTS or similar)
Strong statistical modeling and good judgment on when simple methods beat complex ones
Experience with real supply chain data, ideally at a planning vendor such as o9 Solutions, Blue Yonder, Kinaxis, Oracle or SAP, or at a large manufacturer
Experience shipping models to production, not only notebooks

Nice-to-haves

Multi-echelon inventory, Bayesian methods for demand sensing, or publications in applied OR and ML are a plus

Application prep

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aimlpython

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Effort: quick

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