At Ruth IT GmbH, I owned analytical and machine-learning work from stakeholder requirements through model selection, tuning, validation, and delivery.
I developed a baseline model for highly imbalanced confidential data that achieved approximately 95% accuracy and a 73% F1-score on held-out test data. I also built Python data pipelines and ML/AI components using Microsoft Azure, Azure Virtual Machines, and Azure AI Foundry.
Before industry, I led multi-year quantitative research projects at the University of Vienna, turning open-ended problems into mathematical models, computational approaches, validated results, and clear deliverables.
With a PhD in Mathematics, I bring mathematical modelling, optimization, numerical analysis, statistics, and scientific computing to complex problems. I communicate technical findings clearly to managers and non-technical domain experts while applying GDPR-aware practices for sensitive data.
