Work

I am an economics graduate currently pursuing an M.Sc. in Data Analytics at Justus Liebig University Giessen, with a strong focus on data science, applied machine learning, and data-driven decision-making. My background combines economic expertise with hands-on experience in statistical analysis, predictive modeling, natural language processing, and data visualization.

At the University of Hamburg, I worked as a teaching assistant for Machine Learning for Economics and Finance, where I transformed course materials from R to Python and developed interactive data analysis workflows using Jupyter and RMarkdown. I also contributed to the international research institute DESY, supporting the organization of a scientific conference and developing a Python-based submission tool for peer-reviewed publications.

My academic work includes applying Large Language Models such as GPT-4 and LLaMA 3.1 to classify corporate decarbonization pledges from financial news. Alongside my academic work, I build independent data science projects that apply analytical and machine learning methods to real-world datasets – including an interactive GPX-based running dashboard, an end-to-end e-commerce funnel analysis with predictive modeling, and NLP pipelines using ClimateBERT for the classification of corporate climate commitments.

I enjoy turning complex and unstructured data into meaningful insights and practical solutions – combining analytical rigor, technical implementation, and a strong focus on real-world impact.