Operations Research Scientist

You will drive manufacturing excellence through advanced optimization at Hadrian. As our Operations Research Scientist, you'll tackle complex scheduling, capacity planning, and resource allocation challenges in aerospace manufacturing. Using cutting-edge solvers and potentially RL techniques, you'll build systems that optimize everything from production queues to tool utilization. This role combines rigorous mathematical modeling with real-world impact—your algorithms will directly improve delivery times, reduce costs, and scale our manufacturing capabilities.

What You'll Do

• Design and implement production scheduling algorithms using CP-SAT and MIP solvers.
• Build capacity planning models that balance demand with resource constraints.
• Develop queue management systems to optimize throughput and reduce wait times.
• Create demand forecasting models using modern time series techniques.
• Optimize inventory levels across raw materials and work-in-progress.
• Design tool allocation algorithms considering utilization and maintenance.
• Implement robust optimization for uncertain demand and processing times.
• Explore RL approaches for dynamic scheduling and queue management.
• Build simulation models to validate optimization strategies.
• Develop APIs and tools for real-time decision support.
• Create dashboards to visualize optimization results and KPIs.
• Collaborate with engineers to understand constraints and objectives.
• Design experiments to validate model improvements.
• Document algorithms and train operations teams.
• Stay current with OR research and manufacturing best practices.

What We're Looking For

• Expert knowledge of optimization solvers (OR-Tools, Gurobi, CPLEX).
• Strong background in mathematical optimization (LP, MIP, CP).
• Experience with production scheduling and capacity planning.
• Proficiency in Python and optimization libraries.
• Understanding of manufacturing processes and constraints.
• Experience with forecasting and demand planning.
• Knowledge of queueing theory and simulation.
• Ability to translate business problems into mathematical models.
• Strong statistical and analytical skills.
• Experience deploying optimization models in production.
• Excellent communication skills for technical and non-technical audiences.
• Ability to work with messy, real-world data.
• Understanding of computational complexity and algorithm design.
• Experience with A/B testing and experimental design.