Data Science & AI Specialist (m/f/d) (80-100%)

Accelerate the Energy Transition with Advanced Analytics

Join a global technology leader dedicated to accelerating sustainability in the marine and energy industries. With a heritage of over 100 years and a presence in more than 50 countries, our organization serves customers in over 100 locations. Our 3,000 employees continuously innovate to deliver best-in-class products, services, and solutions that are mission-critical for the energy transition. You will join a team of experts in an exciting international environment, committed to excellence and innovation, supporting customers in driving the transition toward sustainable industries with cutting-edge technology, deep expertise, and smart solutions. We foster diversity and inclusion, welcoming and celebrating individual differences as a source of strength.

Foundation of the Advanced Analytics AI Center of Excellence

This role serves as the foundation for the new Advanced Analytics AI Center of Excellence (CoE). It requires coding experience and the handling of structured and unstructured data sets. The primary objective is to transform business challenges into data-driven solutions by identifying the right data, building and validating models, and translating results into insights and measurable business value. The CoE builds process-agnostic capability and acts as the expert partner to process domain teams and business units that own the solutions. Alongside hands-on delivery, this role helps the AI citizen program across the company to work safely and effectively with AI, helping to reach the next AI data maturity level.

Key Responsibilities

• AI and Machine-Learning Use Case Delivery: Support and deliver data-driven solutions from problem framing and feasibility assessment to model development, validation, deployment, and post-go-live monitoring. Create actionable insights using data science and AI methods, including statistical analysis, machine learning, computer vision, and Large Language Models applied to business problems.
• Solution Handover to Sustainable Operation: Ensure documentation, retraining, and monitoring approaches are established, with clear ownership shared together with process domain and application teams.
• Business Partnership & Value Translation: Work with business units to understand needs, engage with Divisions and Functions, clarify processes to be improved, and challenge assumptions when required. Translate analytical results into business value and actions by explaining findings in business language, quantifying expected benefits, and defining necessary changes. Advise business users end-to-end on data inputs, feature selection, modelling approaches, evaluation of results, and practical project implementation.
• Use Case Qualification: Assess data availability and quality, effort, risk, and expected return to support the demand intake and prioritization process. Ensure compliance with regulations such as the EU AI Act and GDPR, as well as internal security processes.
• Data Sourcing & Platform Collaboration: Identify and source relevant internal and external data, assessing its quality, ownership, and suitability. Work through the Enterprise Data Layer and Data Product Catalogue to consume governed data products when they exist, feeding gaps back to the Enterprise Data Architect and data-product owners rather than building one-off extracts.
• Tooling, Engineering & Standards: Integrate tools such as Python and R into the analytics landscape, including notebooks, libraries, and their use within Microsoft Fabric and the enterprise data platform. Apply sound data engineering practices such as version control, code review, environment management, testing, and repeatable pipelines. Help define the CoE's methods and standards, including reference approaches, templates, model documentation, and evaluation criteria. Evaluate new methods and tools to make pragmatic recommendations on adoption.
• Enablement of Citizen Data Scientists & Knowledge Sharing: Provide guardrails, templates, training, and coaching so business users can apply advanced analytics safely and effectively. Advise the citizen community on method choice, distinguishing when self-service analytics is appropriate versus when a CoE-delivered solution is better. Share best practices and collaborate across Digital and IS teams, including the MS Copilot Studio CoE, process engineers, application owners, and the Digital AI team, to reuse assets rather than duplicate capability. Contribute to communities of practice to raise AI and data literacy across the organization.

How Success Is Measured

• Data Science and AI use cases delivered into production with demonstrable business value.
• Models that are documented, monitored, and maintainable beyond their original author.
• Analytical solutions built on governed data products rather than bespoke one-offs.
• A growing, capable, and well-governed AI citizen community.
• Reusable methods, templates, and assets shared across the CoEs and IS teams.
• Positive feedback from business stakeholders on the usefulness of results.

Required Background and Expertise

Experience

• Practical experience delivering data science and AI solutions in a business context, ideally in an international industrial environment.
• A track record of taking analytical work from exploration through to productive use.
• Experience advising or coaching non-specialist users is an advantage.

Technical Expertise

• Strong command of Python and/or R and common data science libraries, along with solid SQL skills.
• Sound grounding in statistics and machine learning, including supervised and unsupervised methods, forecasting, model evaluation, and validation.
• Experience with cloud analytics platforms, ideally Microsoft Fabric and Azure AI, and with Data Lakehouse and data-pipeline concepts.
• Understanding of enterprise data sources such as SAP ERP, SAP BW, and operational applications, as well as data quality and governance constraints.
• Familiarity with MLOps practices, including versioning, deployment, monitoring, and retraining, and with responsible AI considerations.
• Knowledge of computer vision (manufacturing processes) and Large Language Models is preferred.

Ways of Working and Soft Skills

• Genuine curiosity about the business problem, not only the method.
• Ability to explain analytical results clearly to non-technical stakeholders.
• Pragmatic and delivery-oriented, comfortable starting small and scaling what works.
• Collaborative, willing to share knowledge and work in cross-functional fusion teams.

Education and Languages

• University degree in data science, statistics, mathematics, computer science, engineering, or a comparable field.
• Fluent English required; German is an advantage.

Benefits

• Competitive compensation package.
• Performance-related bonus opportunities for eligible employees.
• Supportive culture focused on trust, accountability, and flexibility.
• Global Parental Leave Program.
• Employee Assistance Program.
• Health and well-being initiatives.
• Company canteen.
• Opportunities to work with global teams and industry-leading technologies in a modern workplace.