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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.