AI/ML Scientist

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.

We Offer

Joining Maersk, you will become part of the global family of the company that moves 20% of global trade every day all the way, where one of our core values is Our Employees. It goes without saying that we value diversity in all its forms, including but not limited to: gender, age, nationality, race, sexual orientation, disability or religious beliefs. We are proud of our diversity and see it as a genuine source of strength for building high performing teams.

• Key Responsibilities:
• 1. Design, develop, and optimize AI-driven automated workflows to enhance business efficiency.
• 2. Collaborate with cross-functional teams to analyze business processes and identify AI automation opportunities.
• 3. Integrate machine learning models, NLP, computer vision, and other AI technologies into enterprise workflows.
• 4. Build and maintain AI pipelines, including data collection, preprocessing, model deployment, and monitoring.
• 5. Develop customized AI tools and interfaces to enable non-technical users to leverage AI capabilities.
• 6. Continuously monitor AI workflow performance metrics and implement improvements.
• 7. Ensure AI systems comply with data privacy and security regulations.

8. Fine-tuning experience is nice to have

Key Requirements:

• Essential Qualifications:
• - Bachelor’s or master’s degree in computer science, AI, or a related field.
• - 2+ years of experience in AI/ML development, with proficiency in frameworks like TensorFlow/PyTorch.
• - Strong Python skills and experience with API development (REST/gRPC) and microservices architecture.
• - Hands-on experience with workflow automation tools (e.g., Dify , N8N, fastGPT, langchain).
• - Excellent problem-solving skills and a data-driven mindset.

• Preferred Qualifications:
• - Experience with RPA tools (UiPath/Automation Anywhere) or low-code platforms.
• - Knowledge of LLM application development (e.g., GPT, Claude-based solutions).
• - Experience with containerization (Docker/Kubernetes) and MLOps practices.
• - Domain expertise in AI implementation for industries like Logistic, FinTech etc...

- Freight forwarding knowledge preferred

• 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 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 accommodationrequests@maersk.com .

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.