Overview As an AI Scientist - Planning, you design and implement AI-driven optimization for planning across demand forecasting, supply planning, inventory optimization, and S&OP. You will bridge AI/ML and operations research to handle complex, multi-echelon supply chains and long lead times, delivering production-ready models. You collaborate with ML engineering and product teams to ship scalable solutions that impact enterprise customers. This is a chance to shape planning intelligence at industrial scale in a fast-growing, innovative setting.
Responsibilities- Build demand forecasting models using probabilistic, hierarchical, and intermittent demand methods
- Design supply planning and inventory optimization models for multi-echelon networks with capacity constraints and variable lead times
- Formulate and solve optimization problems (MIP, LP, constraint programming) for production scheduling, allocation, and resource planning
- Build simulation and scenario-analysis frameworks to support S&OP and integrated business planning workflows
- Define evaluation metrics, construct backtesting pipelines, and run experiments to improve model performance
- Integrate cross-domain signals from Avathon's Computational Knowledge Graph into planning models
- Own the path from research to production in collaboration with ML Engineering, delivering scalable and monitored services
- Mentor junior data scientists on technical depth and OR/ML fundamentals
Key requirements- Ph.D. or master's degree in operations research, Industrial Engineering, Statistics, Computer Science, Applied Mathematics, or a related quantitative field
- 6+ years of industry experience in applied ML, optimization, or supply chain planning systems
- Deep experience in at least two: demand forecasting, supply/inventory optimization, production scheduling, or S&OP analytics
- Proficiency in Python with hands-on experience in optimization solvers (Gurobi, CPLEX, OR-Tools, or similar) or forecasting libraries (Prophet, statsmodels, GluonTS, or similar)
- Strong foundation in statistical modeling with sound judgment on when simple methods outperform complex ones
- Experience with supply chain and operational data - demand history, ERP/MRP transactions, and planning system outputs
- Ability to translate business problems into mathematical optimization or ML formulations
- Experience deploying models into production environments
- Collaboration with cross-functional teams (product, engineering)
- Mentorship and technical leadership
- Problem solving and strategic thinking
- Python
- Optimization solvers (Gurobi, CPLEX, OR-Tools)
- Forecasting libraries (Prophet, statsmodels, GluonTS)
