Senior AI Scientist, Planning

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

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.