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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Ai/Ml Scientist - **Company:** Maersk - **Location:** Morrison, TN, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Artificial Neural Networks, JIRA, Computer Programming, Data Validation, Information Engineering, Cursor, Discrete Event Simulation, R (Programming Language), Integer Programming, Python (Programming Language), Linear Programming, Machine Learning, Natural Language Processing, Software Maintenance, Scientific Computating, SQL Databases, Reinforcement Learning, GitHub Copilot, Claude Code, Deep Learning, Generative AI, Git, Information Technology, Modeling and Simulation, Data Analytics, Operational Systems, Machine Learning Operations, Software Coding - **Published:** November 7, 2026 - **Apply:** https://www.careerbuilder.com/job-details/ai-ml-scientist-morrison-tn--f53e2250-f280-45b7-826a-4267d88400c5 ## About the Role Artificial Intelligence (AI), Data Analysis, Artificial Intelligence (AI), Finance, Automation, Healthcare, Machine Learning, Predictive Modeling, Problem Solving Skills, Statistical Modeling, * 5+ years of industry experience in building and delivering simulation models, optimization solutions, or data science products. * PhD or M.Sc. 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. Nice to have: * 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 operationsExperience in material handling, manufacturing operations, or other domains involving physical asset optimization and sequencing problems. * Experience developing and interacting with generative AI models., CORE SKILLS Data Analysis: The process of inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making Proficiency Level: Proficient Statistical Analysis: The process of collecting and analyzing data to identify patterns and trends, and to make informed decisions. Proficiency Level: Proficient AI & Machine Learning: The field of artificial intelligence (AI) involves creating systems that can perform tasks that typically require human intelligence. Machine learning (ML) is a subset of AI that uses algorithms to learn from and make predictions based on data Proficiency Level: Proficient Programming: Writing code to manipulate, analyze, and visualize data, often using languages like Python, R, and SQL. Proficiency Level: Proficient Data Science: A multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Proficiency Level: Proficient SPECIALIZED SKILLS Data Validation and Testing: Ensuring that data is accurate and meets the required standards before it is used in analysis or decision-making. Model Deployment: The process of making a trained machine learning model available for use in production environments. Machine Learning Pipelines: Automated workflows that manage the end-to-end process of training and deploying machine learning models. Deep Learning: A subset of machine learning involving neural networks with many layers, used to model complex patterns in data. Natural Language Processing (NLP): A field of AI that focuses on the interaction between computers and humans through natural language. Optimization & Scientific Computing: Using Mathematical techniques and computational algorithms to solve complex problems and optimize processes Decision Modeling and Risk Analysis: Decision Modeling and Risk Analysis are methodologies used to make informed, data-driven decisions under uncertainty, especially when multiple factors and possible outcomes need to be considered. Technical Documentation: Creating and maintaining documentation that explains the functionality, use, and maintenance of software or systems. Definition of Proficiency Levels: Foundational: This is the entry level of the skill, typically expected when starting a new role or working with the skill for the first time. You rely on strong manager support, coaching, and training as you build the capability to progress to higher proficiency levels. Proficient: This is the level at which you are considered effective in the skill. You demonstrate more than just functional competence-you begin to have a noticeable impact in your role by applying the skill consistently and meaningfully. You require only minimal support, coaching, or training to apply the skill successfully. Advanced: This is the level where you move beyond meeting expectations to actively leading, influencing, and delivering considerable impact across the wider business. You are seen as a role model, demonstrate the skill independently, and require little to no manager support. ## Description 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., * 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.