Operations Research Analyst

Develop mathematical models and analytical tools to support complex planning, scheduling, resource allocation, and operational decision making.

Build and maintain optimisation models using techniques such as linear programming, integer programming, mixed integer programming, simulation, and other operations research methodologies.

Analyse operational data to identify constraints, bottlenecks, inefficiencies, and opportunities for improvement.

Translate real world business problems into structured mathematical formulations and practical optimisation solutions.

Run scenario analysis and sensitivity testing to evaluate different operating strategies and support informed decision making.

Work closely with business, operations, commercial, and technology teams to understand requirements, validate assumptions, and ensure models are practical and fit for purpose.

Develop tools and workflows using Python, Excel, SQL, or other analytical platforms to automate modelling and decision support processes.

Monitor model performance and continuously refine assumptions, constraints, and optimisation logic as business requirements evolve.

Prepare clear reports, visualisations, and presentations to communicate model outputs, recommendations, and key trade offs to stakeholders.

Maintain documentation covering model methodology, assumptions, workflows, and operating procedures to support long term usability and knowledge transfer.

Bachelor's or Master's degree in Operations Research, Applied Mathematics, Industrial Engineering, Engineering, Statistics, Data Science, or a related quantitative discipline.

Strong foundation in mathematical modelling and optimisation, including exposure to linear programming, mixed integer programming, scheduling, or resource optimisation.

Good understanding of quantitative problem solving, constraints modelling, objective functions, and trade off analysis.

Proficiency in Python or another analytical programming language such as R or MATLAB.

Experience with optimisation tools or solvers such as Gurobi, CPLEX, OR Tools, Pyomo, PuLP, or Excel Solver would be advantageous.

Comfortable working with structured data using SQL, Excel, or relational databases.

Ability to translate complex operational challenges into mathematical models and explain technical findings clearly to non technical stakeholders.

Strong analytical thinking, attention to detail, and a structured approach to problem solving.

Comfortable working cross functionally across technical and business teams.

Experience in planning, scheduling, logistics, supply chain, energy, transportation, manufacturing, or other operationally complex environments would be advantageous.