Simulation & Operational Modelling Scientist

A global technology organisation is seeking a Simulation & Operational Modelling Scientist who wants to apply machine learning, simulation and advanced analytics to solve real-world operational challenges.This isn’t a role where your work disappears into reports. This is an opportunity to work with large-scale operational datasets and develop models that help improve forecasting, planning and decision-making in a high-volume operational environment. You’ll work on complex problems where your analysis has a genuine purpose, developing predictive models, identifying constraints and building simulation tools that help answer important “what if?” questions around future demand and performance. This is a role where you’ll be trusted to take ownership of your work; understanding complex problems, selecting the right approach, validating your results and clearly explaining the reasoning behind your recommendations. You won’t just be producing outputs. You’ll be expected to understand how you got there, why the approach was right and how your insights support operational decision-making, while continuing to develop your technical capability in a complex real-world environment. You’ll be involved in:Developing machine learning models to improve forecasting and operational planningApplying time-series analysis and feature engineering techniques across large datasetsBuilding and improving discrete event simulation models to test different scenariosIdentifying trends, patterns and potential operational constraintsTranslating technical analysis into clear insight and recommendations

Experience required:Applied machine learning and statistical modellingForecasting, simulation, optimisation or operational research techniquesR programming (Python experience beneficial)SQL and working with large datasetsAbility to explain technical findings clearly to different audiences A background within transport, logistics, aviation, engineering, manufacturing, supply chain or similar operational environments would be highly advantageous. This would suit someone who enjoys modelling how things work, testing scenarios and seeing their work influence real-world decisions.

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