Senior Engineer - Data Science

Forvia, a sustainable mobility technology leader We pioneer technology for mobility experience that matter to people. Your mission, roles and responsibilities We are recruiting a Data Scientist to design, build and deploy high-value AI solutions aligned with the strategic AI roadmap. Working with business, engineering, data and IT stakeholders, you will translate operational and Cost Management challenges into production-ready analytical products. Typical use cases include cost forecasting and simulation, should-cost modelling, cost-driver and variance analysis, savings opportunity identification, manufacturing process optimization, automated quality inspection, supply-chain analytics and intelligent product or enterprise solutions. Contribute to AI roadmap execution by defining use cases and prioritizing initiatives according to strategic fit, feasibility, value and scalability. Develop machine-learning, deep-learning, optimization, forecasting, generative AI and agentic solutions using robust experimentation and evaluation methods. Prepare, explore and validate structured and unstructured data; engineer reliable features and ensure data quality, lineage and reproducibility. Industrialize models with software-engineering and MLOps practices, including testing, versioning, deployment, monitoring, drift detection and retraining. Apply responsible-AI, security, privacy and governance requirements throughout the model lifecycle. Promote reuse, knowledge sharing and adoption across international, cross-functional teams. Your profile and competencies to succeed Qualifications and skills Experience: 3–5 years for an experienced profile; 5+ years for a senior profile, with demonstrated delivery of data-science or AI solutions in production. Education: Master’s degree in data science, computer science, applied mathematics, statistics, engineering or a related quantitative field; a PhD is an advantage. Core data science: Strong knowledge of statistics, experimental design, supervised and unsupervised learning, forecasting, optimization and model evaluation. AI: Practical experience with deep learning and at least one advanced domain such as computer vision, natural language processing, generative AI, retrieval-augmented generation or AI agents. Programming: Strong Python skills and experience with SQL; ability to write clean, documented, tested and maintainable production code. Frameworks: Experience with libraries such as pandas, NumPy, scikit-learn, XGBoost, PyTorch or TensorFlow. Production and cloud: Familiarity with APIs, Git, containers, CI/CD, model registries, monitoring and at least one major cloud or enterprise data platform. Ways of working: Knowledge of Agile delivery and effective collaboration with product owners, data engineers, software engineers and domain experts. Communication: Excellent analytical, storytelling and presentation skills, including project reviews with customers and…