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

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 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). Tooling, engineering & standards Integrate tools such as Python and R into the analytics landscape incl. notebooks, libraries, and their use within Microsoft Fabric and the enterprise data platform. Apply sound data engineering practices: version control, code review, environment management, testing, and repeatable pipelines for data preparation and model training. Help define the CoE's methods and standards: reference approaches, templates, model documentation, evaluation criteria, and the path from prototype to production. Evaluate new methods and tools: assess their fit for Accelleron and make pragmatic recommendations on what to adopt. Enablement of citizen data scientists & knowledge sharing Enable AI citizen: provide guardrails, templates, training and coaching so business users can apply advanced analytics safely and effectively. Advise the citizen community on method choice: when self-service analytics is appropriate, when a CoE-delivered solution is the better answer, and where the risks lie. Share best practices and collaborate across Digital and IS teams incl.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 & data literacy across the organization. How success is measured: Data Science & 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-off. 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. Your Background: Experience Practical experience delivering data science and AI solutions in a business context, ideally in an international industrial environment. 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 the common data science libraries; solid SQL. Sound grounding in statistics and machine learning — supervised and unsupervised methods, forecasting, model evaluation and validation (Data science and AI). Experience with cloud analytics platforms, ideally Microsoft Fabric / Azure AI, and with Data Lakehouse and data-pipeline concepts. Understanding of enterprise data sources such as SAP ERP, SAP BW and operational applications, and of data quality and governance constraints. Familiarity with MLOps practices — versioning, deployment, monitoring and retraining — and with responsible AI considerations. Preferably. Knowledge on computer vision (manufacturing processes) and LLM. Ways of working & soft skills Genuine curiosity about the business problem, not only the method. Able 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 to work in cross-functional fusion teams. Education & languages University degree in data science, statistics, mathematics, computer science, engineering, or a comparable field. Fluent English required; German an advantage. Your 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. We look forward to receiving your application. If you want to discover more about Accelleron, take another look at our website . Accelleron Data Privacy Statement: Job Family Group: Information Systems