About me
I am a data scientist at Mücke Roth & Company in Munich, Germany. I have a Master's degree in statistics and a Bachelor's degree in economics.
I am interested in things related to data analysis, e.g. statistics, data science, machine learning, deep learning. Beyond modeling and predicting I think it is also important to interpret results (machine learning interpretability) and consider economic and social implications of a data driven world.
In my spare time, I make electronic music and play the piano. You can also find me on a hike or on a bicycle tour across the Alps.
Work Experience
- Industries: energy, telecommunications, e-commerce, automotive.
- Tasks: Data analysis, data engineering, statistical modeling.
- Tools: Python, SQL, Git, Jupyter, Pandas, GeoPandas, Polars, scikit-learn, QGis.
- Tasks: Data analysis, data engineering, statistical modeling.
- Tools: Python, Jupyter, Airflow, Oracle, Pandas, GeoPandas, Git, Gitlab, Jira, DBeaver
- Developing a text parser that consolidates car status reports for a business intelligence application.
- Tools: Python, PostgreSQL, Git.
- Creation and revision of lecture notes for Computational Statistics and Predictive Modeling (graduate level).
- Weekly tutorial for Computational Statistics (graduate level).
- Tools: R, RMarkdown, Latex, Git.
- Topics: International, industrial economics, studies on innovation.
- Data collection and analysis, literature research, preparation of lecture slides.
- Tools: Stata, LaTex, Microsoft Word / Excel / PowerPoint.
- Weekly tutorials for Econometrics, Microeconomics and Macroeconomics (undergraduate level).
Education
Master thesis title: Estimating Interval-based Feature Effects in Supervised Learning Models
- Method to quantify feature effects in supervised machine learning models by dividing complex, non-linear response functions into automatically detected stable intervals and estimating effects separately within each interval.
- R Package on GitHub