Senior Operations Research Engineer

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