My foundation is quantitative — a BSc in Mathematics and graduate-level coursework in Data Science and Economics, both at the University of Milan and both with top marks. I use that quantitative lens on everything: pricing models, system optimization, product analytics, and strategic prioritization.
I attended a Master's degree course in Data Science and Economics, scoring a perfect GPA of 30/30. Dropped out before the thesis to pursue my startup. Coursework covered Machine Learning, Statistical Learning, Econometrics, and Programming. Capstone project: "Decomposing and Clustering the EU Business Economy" — applying dimensionality reduction and unsupervised clustering to large-scale macroeconomic datasets to identify structural patterns across industries.
Undergraduate degree in Mathematics (GPA: 108/110) and a merit scholarship. This three-year degree (one of the University's hardest, with a 60% dropout rate) was a crucible for rigorous thinking and problem-solving. With a focus on deep theoretical foundations (Algebra, Calculus, Geometry) and applied subjects (Combinatorics, Physics, Optimization), it trained a way of thinking I rely on every day: start from first principles, question assumptions, and don't call something solved until the logic actually holds.
Designed and maintained Metabase dashboards tracking system performance, user behavior, and infrastructure metrics. Wrote advanced SQL queries to diagnose bottlenecks invisible to standard monitoring. The analysis directly guided engineering priorities and resulted in a 50% increase in computing throughput — the kind of improvement only visible when you're looking at the right data in the right way.
Built comprehensive cloud cost-forecast models by synthesizing multi-vendor pricing data across AWS and GCP. Implemented standardized provisioning rules and spending guardrails that reduced compute expenses by 30% without degrading performance SLAs. Applied the same analytical rigor to financial modeling that I'd use on any other optimization problem.