Senior Applied Optimisation Scientist

The Opportunity

We are looking for a highly skilled Applied Optimisation Scientist to design, extend, and validate large-scale mathematical optimisation models supporting strategic mine planning and optimisation platforms. This role focuses on solving complex, high-impact planning problems using advanced optimisation techniques to drive long-term operational and investment decisions. You will work at the intersection of mathematical modelling, software engineering, and domain-specific problem-solving, contributing to mission-critical systems that directly influence business performance. The role requires strong expertise in linear and mixed-integer optimisation, hands-on solver experience, and the ability to translate real-world constraints into scalable optimisation solutions.

Your Key Responsibilities:

• Develop and maintain large-scale LP/MIP optimisation models for mine planning and scheduling.
• Design and implement constraints including capacity, sequencing, blending, cost optimisation, and boundary conditions.
• Support advanced optimisation workflows such as Direct Block Scheduling (DBS).
• Continuously improve model structure and performance for production-grade deployments.
• Integrate and configure optimisation solvers such as Gurobi, Maroma, and AMPL.
• Ensure high performance, scalability, and numerical stability of models.
• Diagnose and resolve infeasible solutions, performance bottlenecks, and instability issues.
• Optimize solver parameters and execution strategies for efficiency.
• Validate optimisation outputs through rigorous testing, cross-validation, and alternate formulations.
• Ensure correctness, reliability, and consistency of outputs.
• Take ownership of solution quality and defend results with strong analytical reasoning.
• Collaborate with product teams, engineers, and domain experts to translate business problems into mathematical models.
• Communicate optimisation assumptions, trade-offs, and outcomes to both technical and non-technical stakeholders.
• Support decision-making by providing insights derived from optimisation models.

Required Skills:

• Strong background in Operations Research, Applied Mathematics, and optimisation techniques.
• Proven experience in Linear Programming (LP) and Mixed Integer Programming (MIP).
• Hands-on experience with Gurobi, Maroma, and AMPL (mandatory).
• Experience with additional solvers such as CPLEX or Xpress is advantageous.
• Strong Python programming skills for model development, integration, and testing.
• Experience handling large-scale, performance-critical datasets.
• Solid understanding of optimisation model performance tuning and stability considerations.

Preferred Skills & Experience:

• Experience in mine planning, supply chain optimisation, or heavy industry use cases.
• Exposure to graph-based optimisation and network flow models.
• Familiarity with enterprise-scale optimisation systems.
• Working knowledge of C# / .NET (preferred).

Core Technology Stack:

• Optimisation Solvers: Gurobi, Maroma, AMPL
• Methods: Linear Programming, Mixed Integer Programming, Network Flow Optimisation, Constraint Modelling
• Programming: Python, AMPL, C#/.NET (preferred)

Key Competencies:

• Strong analytical and structured problem-solving skills
• Ability to solve complex real-world optimisation challenges
• Ownership mindset with strong attention to detail
• Effective communication and stakeholder engagement
• Ability to balance model accuracy with computational efficiency

Education:

Bachelor's or master's degree in operations research, Mathematics, Engineering, Computer Science, or related field.

Skills

AMPL, Gurobi, Maroma, Optimisation, Optimization, Python