I’m a Data Scientist with a background in Machine Learning and E-Commerce, experienced across Germany, Japan, and the U.K. I love turning data into meaningful stories — and when I’m offline, I enjoy playing the violin and knitting.
Feb 2023 - Present
Berlin, Remote
Constellation Academy Holding GmbH unites the expertise of a broad education group to offer comprehensive, modern solutions in learning, coaching, and organizational development.
Feb 2023 - Present
March 2020 - May 2023
Remote, Germany
March 2020 - May 2023
Aug 2016 - Mar 2020
Hamburg, Germany
Otto GmbH & Co KG is a leading German retail and e-commerce group operating a wide range of online shops, logistics services, and marketplace platforms.
Aug 2016 - Mar 2020
2020-2021 M.Sc. in Business AnalyticsTaken Courses:
Thesis:Segmented three customer value groups (high, medium, low) to optimise marketing strategies using the RFM model, K-Means, and DBSCAN on bank transaction data. Supervisor:Prof. Dr. Yi Cao | ||
B.Sc. in E-Commerce (focus Computer Science)Taken Courses:
Thesis:Evaluated the impact of cross-company marketing campaigns, confirming a positive lift for the advertising company without harming the partner brand, using ARIMA modelling and the Wilcoxon rank-sum test on sales data. Supervisor:Prof. Dr. Florian Schatz |
Optimized passenger flight hub location problem with a Reduced Variable Neighbourhood Search (RVNS), solving instances of the Capacitated Single Allocation p-Hub Location Problem.
Improved flight departure delay prediction using a regression decision tree model on flight data from Atlanta International Airport.
Identified key problems and recommendations from Google Play Store user reviews using NLP and sentiment analysis with Long Short-Term Memory (LSTM) Network.
Investigated the reason for revenue decrease by analysing the impact of price changes on customer purchase probabilities using Naives Bayes, SVM, LightGBM, and Python
Segmented three customer value groups (high, medium, low) to optimise marketing strategies using the RFM model, K-Means, and DBSCAN on bank transaction data.
A broad introduction to machine learning, covering key methods in supervised and unsupervised learning, including regression, classification, neural networks, clustering, and recommender systems. The course also explores deep learning fundamentals and essential best practices such as model evaluation and bias–variance tradeoffs.
