Senior Operations Research Engineer

Job description

Key Skills: Machine Learning, Implementation, Operations Research, Optimization, Python, Design, Mixed Integer Programming (MILP/MIP), Mathematical modelling, Mathematical Optimization Roles and Responsibilities: • Lead the design, implementation and delivery of advanced optimization solutionsfor terminal operations including container handling equipment efficiency, yard positioning strategies,vessel loading/unloading sequencing, and truck routing. • Build both operational tools for day-to-day terminal operations and strategic models for long-term planningand decision-making. • Coach and mentor junior team members in optimization techniques and operations research methodologies. • Work with relevant stakeholders to understand terminal operational dynamics and business processes,incorporating their needs into products to enhance value delivery. • Collaborate and communicate model rationale, results and insights with product teams, leadership andbusiness stakeholders to roll out solutions to production environments. • Analyse data, measure delivered value, and continuously evaluate and improve models to increaseeffectiveness and operational impact. • Validate and iterate on optimization solutions against discrete event simulation models of terminal operations, • System design, architecture, and solution design for new features. Skills Required: • 5+ years of industry experience in building and deliveringoptimization solutions • PhD or M.Sc. in Operations Research, Industrial Engineering, Machine Learning, Statistics, AppliedMathematics, Computer Science, or other field related to algorithms and data (or equivalent experience). • Depth in optimization modelling LP, MILP, constraint programming, and/or metaheuristics, applied to problems like scheduling, sequencing, routing, bin packing, and resource allocation. • Fluency implementing these in Python across open-source and commercial solvers (e.g. PuLP, OR-Tools/CP-SAT, HiGHS, Gurobi). • Experience developing optimization models for stochastic operational environments, and evaluating them against simulation. • Track record of delivering production-quality Python. • Ability to understand complex operational systems and translate business requirements into effectivetechnical solutions. • Experience in leading technical work, coaching junior colleagues, and driving projects from concept todelivery. A strong plus: • Experience in container terminal operations, port logistics, or similar operational environments withcomplex resource allocation and scheduling dynamics. • Familiarity with container handling equipment, yard operations, or vessel operations. Experience in material handling, manufacturing operations, or other domains involving physical assetoptimization and sequencing problems. • Discrete Event Simulation, AI/ML methods for operational problems, Prescriptive analytics (e.g., stochastic optimization, reinforcement learning). • Experience developing and interacting with generative AI models. Education: Bachelor’s Degree in related field