Required qualifications • Advanced degree in engineering, mathematics, computer science, operations research, or another quantitative field • Significant experience leading applied AI, decision intelligence, optimisation, or analytical platform work in complex operational domains • Strong understanding of optimisation and modelling principles and how algorithms support real planning problems • Experience improving planning, routing, scheduling, or network optimisation through heuristics, analytics, or AI-based enhancements • Strong Python skills and comfort working closely with software and data engineering teams • Proven ability to lead experimentation, validate model performance, and convert technical ideas into business-relevant capabilities • Strong communication skills with the ability to explain technical trade-offs to non-technical stakeholders Classification: Internal Preferred qualifications • Experience in logistics, supply chain, shipping, transportation, or another optimisation-heavy industry • Exposure to large-scale network design, routing, fleet planning, cargo flow optimisation, or similar decision systems • Experience using AI or ML to improve optimisation performance, such as learning-augmented algorithms, surrogate models, search guidance, or reinforcement learning • Experience working across research, prototyping, and productionisation of optimisation methods Leadership expectations This role is expected to lead through others. Using Machine learning (ML), a subset of AI that uses algorithms to learn from and make predictions based on data Proficiency Level: Advanced Data Analysis: Inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making Proficiency Level: Proficient Machine Learning Pipelines: Using automated workflows that manage the end-to-end process of training and deploying machine learning models.