AI/ML Scientist(Operations Research)
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Description
Job Summary
Data AI/ML (Artificial Intelligence and Machine Learning) Engineering involves the use of algorithms and statistical models to enable systems to analyze data, learn patterns, and make data-driven predictions or decisions without explicit human programming. AI/ML applications leverage vast amounts of data to identify insights, automate processes, and solve complex problems across a wide range of fields, including healthcare, finance, e-commerce, and more. AI/ML processes transform raw data into actionable intelligence, enabling automation, predictive analytics, and intelligent solutions. Data AI/ML combines advanced statistical modeling, computational power, and data engineering to build intelligent systems that can learn, adapt, and automate decisions.
AI/ML Scientist (Operations research/Optimization/Simulation)
Role Overview - AI/ML Scientist role focusing on developing advanced simulation models, optimization algorithms, and AI/ML solutions to create operational insights for container shipping terminals worldwide.
- Design, implement and deliver advanced simulation models and optimization solutions for terminal operations including container handling equipment efficiency, yard positioning strategies, vessel loading/unloading sequencing, and truck routing.
- Develop and maintain efficient applied engineering solutions that solve real-world operational challenges at container terminals globally.
- Build both operational tools for day-to-day terminal operations and strategic models for long-term planning and decision-making.
- 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 and business stakeholders to roll out solutions to production environments.
- Analyse data, measure delivered value, and continuously evaluate and improve models to increase effectiveness and operational impact.
- 5+ years of industry experience in building and delivering simulation models, optimization solutions, or data science products.
- PhD or MSc in Operations Research, Industrial Engineering, Machine Learning, Statistics, Applied Mathematics, Computer Science, or other field related to algorithms and data (or equivalent experience).
- Strong experience in simulation modeling and optimization, particularly in operational environments with complex dynamics.
- Research or industry experience in one or more: Discrete event simulation, Combinatorial optimization problems (e.g., scheduling, vehicle routing, bin packing, sequencing), Continuous optimization problems (e.g., linear programming, mixed-integer programming, convex programming), AI/ML methods for operational problems, Prescriptive analytics (e.g., stochastic optimization, reinforcement learning).
- Experience with AI development tools and code assistants (GitHub Copilot, Cursor, Claude Code, or similar) for enhanced productivity.
- Excellent record of delivery using Python and technologies like Git, JIRA, etc.
- Ability to understand complex operational systems and translate business requirements into effective technical solutions.
- Strong communication skills with ability to work independently with stakeholders while liaising and reviewing with the team.
- Experience working with cloud environments.
- Experience in container terminal operations, port logistics, or similar operational environments with complex 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 asset optimization and sequencing problems.
- Experience developing and interacting with generative AI models.
- Data Analysis
- Statistical Analysis
- AI & Machine Learning
- Programming
- Data Science