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.
A.P. Moller - Maersk
A.P. Moller – Maersk is the global leader in container shipping services. The business operates in 130 countries and employs 80,000 staff. An integrated container logistics company, Maersk aims to connect and simplify its customers’ supply chains.
Today, we have more than 180 nationalities represented in our workforce across 131 Countries and this mean, we have elevated level of responsibility to continue to build inclusive workforce that is truly representative of our customers and their customers and our vendor partners too.
The team - who are we:
You will join a technical team of around 50 people in APM Terminals, building the systems behind some of the world's leading container terminals — where the cranes move, the yard fills up, and the vessel is waiting.
What makes this work inspiring is how close it sits to the operation. Your models are not evaluated in isolation: they are discussed with the people who run the terminal and measured against what actually happens there. Getting that right is demanding, and it is what makes the results worth something.
Our backgrounds span operations research, simulation, machine learning, software engineering, and terminal operations themselves . Nobody here covers all of it, and that is deliberate — the most interesting problems tend to sit between two people's expertise.
You will be part of the APM Terminals technical team. As an Applied Scientist, you will have a key role in designing, building, maintaining , and iterating on data-driven products that directly impact terminal operations. This position offers a unique opportunity to apply your technical knowledge to create operational and strategic insights that are transforming container terminal operations globally. This is an exciting time to join a growing and dynamic team that solves some of the toughest problems in terminal operations and builds the future of container shipping. We offer a unique opportunity to impact global trade via world-leading container terminals.
Design, implement and deliver advanced solutions for terminal operations including container handling equipment efficiency, yard positioning strategies, vessel loading/unloading sequencing, and vehicle routing — contributing to system design, architecture, and solution design as new features take shape.
What You Bring (The “T-Shaped” Profile)
We are looking for hybrid problem-solvers . We do not expect you to know everything; rather, we want to see a solid technical foundation combined with real depth somewhere.
The Core Foundation (Required)
Experience : We typically look for 5+ years of industry experience building and delivering technical or optimization solutions. This is a guideline, not a filter — a strong PhD, or exceptional demonstrated ability, can substitute for part of it. We do expect some industry experience: you should have shipped something real that people depend on.
Education : M.Sc. or PhD in Operations Research, Industrial Engineering, Machine Learning, Statistics, Applied Mathematics, Computer Science, or a related quantitative field — or equivalent practical depth.
Production Python : Track record of delivering production-quality Python code for data-centric applications, specifically integrating models ( e.g. machine learning, optimization, simulation, or statistical logic) into functional software.
We are looking for depth, breadth, or a mix of the two. Either works:
Operations Research (OR) : Depth in optimization modelling — LP, MILP, constraint programming, and/or metaheuristics, applied to problems like scheduling, sequencing, routing, bin packing, and resource allocation. Fluency implementing these in Python across open-source and commercial solvers ( e.g. PuLP , OR-Tools/CP-SAT, HiGHS , Gurobi).
Statistical Modelling : Fitting and validating statistical models of operational behaviour — dwell time distributions, arrival processes, equipment cycle times — and applying them to capacity analysis, as inputs to simulation models, or for inference.
Not required, but it will accelerate your ramp-up:
Container terminal operations, port logistics, or comparable operational environments with complex resource allocation and scheduling dynamics — container handling equipment, yard operations, or vessel operations.
Maersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race, colour, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, pregnancy or parental leave, veteran status, gender identity, genetic information, or any other characteristic protected by applicable law. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements.
We are happy to support your need for any adjustments during the application and hiring process. If you need special assistance or an accommodation to use our website, apply for a position, or to perform a job, please contact us by emailing [email protected] .
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.
