Operational Research Scientist

• Formulate high-value optimisation problems from the applied AI backlog, translating operational challenges into clear objectives, decision variables, constraints, assumptions, and success measures.
• Select and justify the most appropriate optimisation approach for each problem, including mixed-integer programming, constraint programming, column generation, metaheuristics, simulation, or hybrid methods.
• Implement robust optimisation models in Python using production-grade solvers such as Gurobi, CPLEX, COPT, OR-Tools, or equivalent platforms.
• Improve solver performance at client-data scale through decomposition, warm starts, cut management, tolerance settings, and practical performance tuning.
• Partner with engineering teams to build reliable data pipelines from client operational systems to model-ready inputs, including validation rules that prevent poor-quality data from entering the solver process.
• Deliver maintainable model code with clear structure, automated tests, version control, documentation, and handover readiness for production use.
• Validate model outputs with business stakeholders to ensure operational feasibility, explainability, and alignment with real-world decision-making constraints.
• Run scenario and sensitivity analyses to show how recommendations change under different demand, capacity, cost, disruption, or policy conditions.
• Refine formulations across delivery iterations using client feedback, operational findings, and performance results.

• Master’s or PhD degree in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, Statistics, or a closely related quantitative discipline.
• At least 3 years of applied experience delivering optimisation solutions for industrial or operational problems, with practical impact beyond academic research or prototypes.
• Around 8 years of experience in engineering
• Proven ability to formulate and deploy at least one production-grade mixed-integer programming, constraint programming, or equivalent optimisation model.
• Hands-on experience with at least one modern solver platform, such as Gurobi, CPLEX, COPT, OR-Tools, or an equivalent optimisation toolkit.
• Strong Python proficiency, with the ability to write structured, maintainable, well-tested model code using version control and sound software engineering practices.
• Ability to explain model formulations, assumptions, constraints, trade-offs, and recommendations clearly to non-technical operations stakeholders.
• Fluent English communication skills for professional, cross-functional, and client-facing working environments.

Preferred:

• Experience solving aviation or transportation optimisation problems, such as crew pairing, crew rostering, fleet assignment, tail assignment, gate or stand allocation, maintenance scheduling, disruption recovery, revenue management, or network planning.
• Knowledge of large-scale optimisation techniques, including column generation, Benders decomposition, branch-and-price, Lagrangian relaxation, decomposition heuristics, or hybrid exact-heuristic methods.
• Simulation experience, such as discrete-event or agent-based simulation, for operational problems where optimisation alone does not fully capture system behaviour or uncertainty.
• Exposure to COPT or other domestic and regional solver ecosystems is an advantage.
• Recognised technical credibility demonstrated through publications, conference participation, industry research, open-source contributions, optimisation competitions, or equivalent achievements.

• A defined career development pathway within a global organization operating across aerospace, smart city, marine, and digital systems.
• Competitive total compensation package

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