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High due to Tier-1 brand, metro location, and mid-level experience expectation.
Medium because OR/optimization skills transfer across logistics and manufacturing, though terminal domain knowledge is preferred.
High due to explicit 5+ years, PhD preference, and required specialized optimization and simulation expertise.
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Lead design, implementation, and delivery of advanced simulation models and optimization solutions for container terminal operations including equipment efficiency, yard positioning, vessel sequencing, and truck routing.
Develop and maintain engineering solutions and AI/ML products that provide operational and strategic insights, improving terminal efficiency and business value globally.
Collaborate with stakeholders to integrate operational needs into models, communicate insights, and drive deployment and continuous improvement of solutions.
5+ years industry experience building and delivering simulation, optimization, or data science products.
PhD or M.Sc. in Operations Research, Industrial Engineering, Machine Learning, Statistics, Applied Mathematics, Computer Science, or related field (or equivalent experience).
Strong expertise in simulation modeling, combinatorial and continuous optimization, AI/ML methods for operational problems, and prescriptive analytics.
Proficiency with Python, AI development tools (e.g. GitHub Copilot), cloud environments, and software engineering tools like Git and JIRA.
Experienced in operational environments with complex dynamics, preferably with knowledge of container terminal or port logistics operations.
Capable of translating complex operational systems and business requirements into effective technical AI/ML and optimization solutions.
Strong communicator who can work independently with stakeholders and drive product delivery and adoption in a multi-disciplinary team environment.