Liechtenstein
As a motivated lead data scientist with a broad background in computer science from ETH Zurich, I have a passion for understanding and solving complex problems. My approach involves collaborating with stakeholders to gain a thorough understanding of their needs before applying an agile methodology to deliver solutions. With expertise in data gathering, predictive modeling, and data processing, manipulation, and visualization, I excel at translating business and functional requirements into tangible results.
I lead the build‑up of Data & Analytics capabilities within the F&P Solutions Business Unit, defining the strategic vision and implementing a scalable end‑to‑end data and AI architecture. I drive the BU’s shift toward data‑driven decision making by delivering impactful analytics solutions and embedding modern data practices across teams.
As the technical lead of Hilti's global IT Data Science team, I steer and provide technical guidance and consultancy across all projects. From initial evaluation to shaping the strategic direction of upcoming projects, I play a pivotal role in ensuring alignment with organizational goals and leveraging the latest advancements in DS/AI methodologies.
Engaged in the Hilti Global IT PhD program, I'm conducting research in the field of IoT battery health tracking and prediction. My publications feature a novel framework utilizing low-frequency utilization-based IoT data, enabling continuous battery health monitoring. This framework, known as the Battery Health Index, not only tracks the State of Health (SoH) but also identifies factors contributing to health degradation based on user behavior. Furthermore, the model enables precise prediction of the remaining useful lifetime (RuL).
Developed a human-machine learning loop platform to predict high-performing prototypes using machine learning models. The platform enabled more accurate testing and faster iteration, resulting in significant cost and time savings.