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Tier-1 brand, metro location, mid-level ML role; niche OR specialization reduces broad applicant volume.
High domain specificity: operations research, simulation, and terminal logistics skills are industry-specialized and less transferable.
Explicit 5+ years requirement, PhD/MSc preference, and specialized optimization and simulation skills.
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Design, implement, and deliver advanced simulation models, optimization algorithms, and AI/ML solutions to improve container terminal operations including equipment efficiency, yard strategy, vessel loading/unloading, and truck routing.
Lead development of operational tools and strategic models for terminal operations impacting container handling and logistics globally.
Collaborate with stakeholders to translate operational needs into technical solutions and continuously evaluate model performance to enhance operational impact.
5+ years of industry experience in simulation modeling, optimization solutions, or data science product development.
PhD or M.Sc. in Operations Research, Industrial Engineering, Machine Learning, Statistics, Applied Mathematics, Computer Science, or related field (or equivalent experience).
Strong experience with simulation and optimization in complex operational environments; proficiency in Python and tools like Git, JIRA.
Experience working with cloud environments and AI development tools or code assistants (e.g., GitHub Copilot, Cursor, Claude Code).
Experienced in simulation, combinatorial and continuous optimization, and AI/ML methods applied to operational or logistics contexts, preferably container terminal operations or similar.
Capable of independently understanding complex operational systems and business processes, translating them into technical solutions with stakeholder engagement.
Comfortable leading delivery of data-driven operational products in a fast-evolving, global logistics environment with cross-functional collaboration.