Greater Munich Metropolitan Area
As Head of Data Analytics and AI, I lead cross-functional teams to turn complex data ecosystems into actionable intelligence and scalable solutions. My mission: drive business transformation through AI, advanced analytics, and digital products — from concept to impact. Over the course of my career, I’ve built and scaled data and AI functions across high-tech and asset-intensive industries, including energy, manufacturing, and mobility — with clear business results like €7M+ in operational savings and improved sustainability outcomes. My approach balances reliable data infrastructure with clear stakeholder coordination, so the solutions we build are sustainable and repeatable. At thyssenkrupp my key focus areas are: - Leading diverse teams across AI, software, and data engineering. - Delivering digital products for customer-facing services and for improving efficiency and performance of industrial production. - Driving AI/analytics initiatives in domains like green hydrogen, electrolysis, and chemie processes- Establishing robust, secure data platforms and governance frameworks - Advising C-level leadership on aligning data strategy with long-term business goals - Collaborating with academia to advance trustworthy, industrial-grade AI (e.g., Konrad Zuse School of Excellence)
- Built and led a team of 14 data & ML engineers across Munich and Aachen, delivering AI-powered process mining innovations for enterprise SaaS clients. - Designed the technical ML roadmap, integrating AI-driven insights into SaaS platforms and enhancing data analytics capabilities. - Established efficient development and workflow practices across ML projects by putting in place MLOps as well as coding standards, review processes, QA, and testing. - Led research partnerships with top German universities, driving innovation in process intelligence. - Besides my technical responsibilities, I was also responsible for talent management, coordinating and facilitating training and identifying development needs incl. performance reviews, feedback, and coaching.
- Responsible for the end-to-end process including requirements specification, definition of technology stack, managing deliverables, and presentations in the area of artificial intelligence and data analytics. - Acquiring and planning budget in the five to six digits ranges for projects in the area of artificial intelligence and data analytics. - Designing and developing machine learning techniques and software solutions for temporal sensor data in the field of predictive maintenance. The developed concepts have been applied in the fields of smart infrastructure, Energy and additive manufacturing. - Concept draft, design as well as development of reliable machine learning methods to detect as well as to anonymize sensitive information from heterogenous data sources such as images or pdfs in the field of data privacy. - Contributing to the strategic topics of Explainable AI and data privacy, covering individual research, cooperation with universities (XAI) and supervision of students. - Driving the research on the topic of Continual AI by developing deep neural network concepts with the ability of an automatic and adaptive learning process enabling machine learning models to be leveraged in dynamic real-world environments. - Demonstrated ability to innovate and think creatively, as testified by six patents in the field of industrial AI and explainability.
My research is broadly focused on event/anomaly detection and prediction models on time series data. I am also interested in graph mining. From the technical side, I have developed methods covering supervised and unsupervised machine learning algorithms, including neural network principles exploiting Deep Learning (e.g. Tensorflow) and Cloud Computing (e.g. Azure) frameworks.
- Worked on automated robust models to identify anomalous behavior in recomendation systems. - Worked on automatic event detection in social media.