Data science, grounded in statistical inference. Econometrics and Bayesian methods from an economics degree, applied to machine learning — built end to end, documented, and shipped.
Bachelor thesis: nowcasting US recession probability from macroeconomic, financial, and text-based predictors — estimated under both Bayesian and frequentist frameworks, a head-to-head comparison of the two paradigms on one forecasting problem.
write-up available on request
A complete pipeline on real, messy data: scraping fbref and Transfermarkt, feature engineering, gradient-boosted regression with randomized search, and a feature-importance analysis of what actually drives player valuations.
A variational autoencoder built as structured software, not a notebook — CLI, data loaders, loss modules — generating MNIST digits in grayscale and color. Variational inference in practice: KL divergence, the reparameterization trick, learned latent spaces. Graded A.
Product-department internship at a B2B fintech — buy-now-pay-later for business trade.
Python tooling (Pandas, NumPy, Matplotlib) for R&D measurement analysis; automated hazardous-material labeling with an Excel/VBA tool the employer flagged as a standout; built automation for a department digitalization push; collected measurement data used to train AI models.
Self-directed quantitative specialization: econometrics, multivariate data analysis, empirical methods, market design. Thesis at the Institute of Econometrics and Statistics.
Looking for a graduate data science role starting mid-2027 — Oslo, Germany, or elsewhere in Europe. If your team does rigorous inference on real business problems, let's talk.