Join EY-GDS Consulting's AI and Data team as a Senior Applied Optimisation Scientist. This role is central to designing, enhancing, and validating large-scale mathematical optimisation models for strategic mine planning and optimisation platforms. You will tackle complex, high-impact planning challenges, driving critical long-term operational and investment decisions through advanced optimisation techniques. This position offers a unique blend of mathematical modelling, software engineering, and industry-specific problem-solving within mission-critical systems that significantly influence business performance. We seek expertise in linear and mixed-integer optimisation, practical solver experience, and the ability to translate real-world constraints into scalable optimisation solutions.

Develop and maintain sophisticated LP/MIP optimisation models for mine planning and scheduling, incorporating constraints for capacity, sequencing, blending, cost, and boundaries. Design and integrate optimisation solvers like Gurobi, Maroma, and AMPL, ensuring high performance and scalability. Validate optimisation outputs rigorously through testing and analysis, guaranteeing correctness and reliability. Collaborate closely with product teams and domain experts to translate business needs into mathematical models, communicating insights effectively to diverse stakeholders.

A strong foundation in Operations Research, Applied Mathematics, and optimisation techniques is essential, alongside proven experience in Linear Programming (LP) and Mixed Integer Programming (MIP). Mandatory hands-on experience with Gurobi, Maroma, and AMPL is required. Proficiency in Python for model development, integration, and testing is crucial, coupled with experience in managing large-scale, performance-critical datasets. A solid understanding of optimisation model performance tuning and stability is also necessary. A Bachelor’s or Master’s degree in operations research, Mathematics, Engineering, Computer Science, or a related field is required.