Optimisation AI Scientist
Owns technical delivery of solver pilots and develops production-grade optimisation capabilities for supply chain and operational planning. The role combines mathematical modelling, Python-based solver benchmarking, customer implementations, and collaboration with engineering, data science, and solver vendors.
About the job
Responsibilities
- Formulate supply chain optimisation problems as rigorous mathematical models, including objective functions, costs, penalties, and constraints.
- Configure and run optimisation models for customer pilots and implementations.
- Support Solutions Architects in delivering results and debriefs.
- Build and run Python models against third-party solvers across multiple dataset scales.
- Benchmark results across use cases.
- Work with Engineering and Data Science leads to define the path to production-grade solver integration and customer-specific implementations within Pigment's architecture.
- Engage technically with solver vendor teams during partnership evaluation.
Requirements
- Demonstrable experience formulating and solving optimisation problems for real-world operational use cases.
- Proficiency in Python with hands-on experience using a major solver library such as Gurobi, OR-Tools, CPLEX, HiGHS, or equivalent.
- Experience with supply chain and operations data, including SKUs, BOMs, capacity constraints, service levels, lead times, and inventory targets.
- Understanding of core operations research techniques, including branch-and-bound, LP relaxation, constraint programming, and sensitivity analysis.
- Degree in Operations Research, Applied Mathematics, Industrial Engineering, Computer Science, or a related field.
Nice to Have
- Master's or PhD in a relevant field.
Skills
Python, Gurobi, Or-Tools, Cplex, Highs, Supply Chain Optimization, Operations Research, Branch-And-Bound, Lp Relaxation, Constraint Programming, Sensitivity Analysis, Mathematical Modeling
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