Data Science & AI Specialist

Accelleron is accelerating sustainability in the marine and energy industries as a global technology leader in turbocharging, fuel injection, and digital solutions for heavy-duty applications. Building on a heritage of over 100 years as a trusted industry partner, the company serves customers in more than 100 locations in over 50 countries. Accelleron’s 3,000 employees are continuously innovating 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. Together, we support our customers in driving the transition toward sustainable industries with cutting-edge technology, deep expertise, and smart solutions. At Accelleron, we foster diversity and inclusion, welcoming and celebrating individual differences as a source of strength.

This role is the foundation of Accelleron's new Advanced Analytics AI Center of Excellence. This role requires coding experience and handling of structured and unstructured data sets. This role transforms business challenges into data-driven solutions: identifying the right data, building and validating models, translating results into insights and measurable business value.

The Advanced Analytics AI CoE builds process-agnostic capability and acts as the expert partner to the process domain teams and business units that own the solutions. Alongside hands‑on delivery, the role helps the AI citizen program across the company to work safely and effectively with AI. These roles will help to go up to the next AI data maturity level.

Your 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 & AI methods: statistical analysis, machine learning, computer vision, Large Language Models applied to business problems.
• Solution Hand over to sustainable operation: documentation, retraining and monitoring approach, and clear ownership together with the process domain and application teams.

Business partnership & value translation

• Work with business units to understand their needs: engage with Divisions & Functions, clarify the actual process to be improved, and challenge when required.
• Translate analytical results into business value and actions: explain findings in business language, quantify the expected benefits, and define what should change as a result.
• Advise business users end to end: from data inputs, feature selection, modelling approaches, evaluation of results, and practical project implementation.
• Contribute to use case qualification: assess data availability and quality, effort, risk and expected return, and support the demand intake and prioritization process. It should follow the regulations in place (e.g EU AI Act, GDPR) and internal processes (e.g Security).

Data sourcing & platform collaboration

• Identify and source relevant data: locate the right internal and external data, assess its quality, ownership and suitability, and prepare it for analytical use.
• Work through the Enterprise Data Layer and Data Product Catalogue: consume governed data products when they exist, feed gaps back to the Enterprise Data Architect and data‑product owners rather than building one‑off extracts (Create once, deploy multiple times).
• Identify and source relevant data: locate the right internal and external data, assess its quality, ownership and suitability, and prepare it for analytical use.
• Work through the Enterprise Data Layer and Data Product Catalogue: consume…