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Tier-1 brand and metro location increase competition, but niche OR specialization reduces applicant density.
Strong operations-research and terminal-logistics specialization limits transferable candidate pool.
Explicit 5+ years, required optimization expertise, solver proficiency and production Python raise shortlisting strictness.
Lead design and delivery of advanced optimization solutions for container terminal operations including equipment efficiency, yard positioning, vessel sequencing, and truck routing.
Build operational and strategic models to improve terminal day-to-day operations and long-term planning.
Coach junior team members and collaborate with stakeholders to embed technical solutions in production environments, measure impact, and iterate improvements.
5+ years industry experience delivering optimization solutions.
PhD or M.Sc. in Operations Research, Industrial Engineering, Machine Learning, Statistics, Applied Mathematics, Computer Science, or related field (or equivalent experience).
Proficiency in optimization modeling (LP, MILP, constraint programming, metaheuristics) and implementation in Python using solvers like PuLP, OR-Tools, Gurobi.
Experience with stochastic optimization models evaluated against simulation; ability to translate complex operational requirements into technical solutions.
Senior-level engineer comfortable leading technical projects from concept to delivery with mentorship experience.
Strong operational understanding of complex resource allocation/scheduling, ideally with container terminal or similar logistics experience.
Skilled in both optimization and simulation methods with familiarity in AI/ML approaches for operational improvements.