I am a Data Scientist and Machine Learning Engineer with a strong background in mechanical engineering, scientific data analysis, and applied AI. My work focuses on developing machine learning models, analyzing complex datasets, building predictive pipelines, and transforming research data into practical insights.

During my PhD research at TU Berlin / ATB Potsdam, I worked extensively with sensor data, CFD simulation results, image-based airflow analysis, and explainable AI methods. I have experience with Python, pandas, NumPy, scikit-learn, XGBoost, LightGBM, CatBoost, Streamlit, FastAPI, and data visualization tools.

I can support projects involving data cleaning, exploratory data analysis, machine learning model development, feature engineering, model evaluation, visualization dashboards, and AI-based decision support systems. I am especially interested in applied AI, engineering data, computer vision, and real-world predictive modeling.

I am reliable, detail-oriented, and able to work independently as well as in interdisciplinary teams. My goal is to help companies use data more effectively to solve practical problems and improve decision-making.