CS + Management graduate from LUISS Guido Carli, Rome. I build end-to-end — ML APIs, RAG systems, data pipelines, multiplayer games. Six shipped projects, all on GitHub. Looking for a team where I can own real problems.
I graduated in July 2026 from LUISS Guido Carli with a dual-track degree in Management and Computer Science. The combination wasn't accidental — I wanted to understand both why data matters and how to build the systems that produce it.
My projects span the full stack of applied ML: scraping and normalising real data, training models, exposing them through REST APIs, containerising with Docker, and shipping live demos. I build things that actually run.
Currently based in Rome, preparing to relocate. Targeting junior roles in Zurich, Basel, Zug or Milan. Non-EU national — open about it, targeting employers who sponsor.
Tools I've actually used in projects — not just listed.
End-to-end builds — from idea to deployed code.
Automated pipeline that aggregates 200+ live listings from RemoteOK and Adzuna APIs, deduplicates by normalised URL, and scores each role 0–100 against a candidate profile via TF-IDF cosine similarity. Served through a JWT-authenticated FastAPI backend, containerised with Docker. Built it to solve my own job search.
2–8 player co-op horror game with a procedurally generated 18×18 DFS maze, host-authoritative Netcode multiplayer, NavMesh enemy AI, and modular day/night cycle in Unity 6 URP. Sole developer — targeting Steam.
End-to-end RAG system over regulatory PDFs: document ingestion → chunking → FAISS vector search → FastAPI REST → Streamlit UI. Containerised with Docker + GitHub Actions CI/CD.
Customer-churn classifier trained with reproducible scikit-learn pipelines, deployed via FastAPI endpoint, and surfaced in a Streamlit dashboard for non-technical stakeholder review.
Joined multi-source geo and sales data in KNIME; built territory scoring logic to surface high-potential targeting zones. Delivered a Power BI dashboard and written workflow document as client-facing output.
Quantitative economics research using real EU data.
Panel analysis of 21 Italian NUTS 2 regions over 2015–2024. Used a continuous DiD framework and event-study design to estimate the differential economic impact of COVID-19 on high-tourism versus low-tourism regions. Identified structural labour market hysteresis as a mechanism for persistent post-pandemic divergence. Supervisor: Prof. Diletta Topazio, LUISS Guido Carli.
Actively looking for junior roles. If you're hiring or want to chat about a project, reach out.