Data Science Engineer

Are you a data-driven engineer who wants to improve complex offshore operations through automated decision support and practical software solutions?

At DSE, we automate the boring and strengthen informed decision-making. We turn data and processes into meaningful tools that help optimise OE’s business. We excel at delivering software solutions that empower the organisation.

As a DSE-er, you will turn engineering knowledge, operational data and advanced algorithms into practical solutions for global tenders, engineering studies and offshore projects. Depending on the challenge, you will work on:

• Data Integration & Visualisation
• Generative AI, including LLMs, NLP & Machine Learning
• Planning, Simulation & Optimisation
• Engineering Business Optimisation
• Software Engineering

You will develop Python-based models and applications using platforms such as Azure DevOps and Databricks. Working closely with our Offshore Energy business units, you will help optimise project execution, fleet deployment and the performance of offshore assets such as our Bokalift crane vessels.

We are looking for someone with a background in engineering, applied mathematics, computer science, operations research or a related field, with experience or a strong interest in data science, simulation, optimisation, generative AI or Python software development.

Experience in offshore energy or maritime contracting is an advantage, but not essential. More important are curiosity, analytical thinking and the ability to translate engineering and business challenges into reliable, transparent and usable solutions. We encourage continuous learning and provide on-the-job support to help you develop your expertise.

Your responsibilities as a data science engineer:

• Develop and implement DSE solutions with and for the Offshore Energy business units, improving the commercial and operational performance of tenders and projects.
• Develop our capabilities in planning, simulation, and optimization, considering factors such as fleet capacity, weather conditions, and unforeseen events.
• Develop and improve Python models and applications in close collaboration with colleagues from the business.
• Integrate and visualize data to create web applications and dashboards that support engineering and operational decision-making.
• Apply generative AI, including LLMs and NLP, as well as machine learning, where these technologies add practical business value.
• Apply professional software-engineering practices to create reliable, reusable, and maintainable solutions.

We will guide and coach you to work in our development toolchain and the Boskalis business, for you to quickly: 

• Identify opportunities for data integration and visualisation, generative AI, including LLMs and NLP, machine learning, simulation, optimization, and automation.
• Understand the needs of multidisciplinary project teams and independently translate them into practical DSE solutions.
• Combine engineering knowledge, data, and algorithms to improve business and operational performance.
• Act as a knowledgeable DSE partner for other departments by sharing expertise and inspiring the effective use of data-driven solutions.

Your qualities
You can make your mark as data science engineer if you have:

• An MSc degree in computer science, applied mathematics, econometrics, computational engineering, or a related engineering discipline, such as civil, offshore, dredging or applied physics.
• Experience in analyzing data and developing data-driven design-decision models using methods such as machine learning, engineering optimisation, constraint programming or advanced metaheuristic algorithms, discrete-event simulation, finite-element modelling.
• Experience in developing Python-based software is essential.
• Experience with Docker, Kubernetes, PySpark or Databricks is an advantage, but not required.
• A proactive attitude, the ability to collaborate effectively within the team and the wider organisation, and a strong curiosity and drive to learn.
• A good command of written and spoken English.