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“It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you are. If it doesn’t agree with experiment, it’s wrong.” ― Richard P. Feynman ―

My Professional Background

I have 10+ years of professional background in applying state-of-the-art machine learning and deep learning algorithms in academic research and in industry. As a researcher in the field of medical physics, I have developed and published various statistical data analysis and modeling techniques for use cases related to signal processing, time-series analysis and medical imaging. As a data scientist in the renewable energy industry and medical technology, I implemented multiple machine learning models to solve use cases related to marketing analytics (e.g. customer segmentation, value or churn prediction), predictive maintenance or disease prediction. Further, I consulted various specialist departments on translating business problems into well-defined data science use cases as well as supporting machine learning engineers, data engineers and software developers with the deployment and monitoring of the ai-models.