Curriculum vitae.

Mercitalia Rail crew-management transformation · IVU.Rail · Italy, hybrid

• Supported end-to-end execution across eight programme phases (3, 4, 7, 8, 9, 10, 11, 12), spanning system foundations, optimisation algorithms, and final module rollouts.
• Contributed directly to closing phases 4, 7, and 10; prepared governance documentation and followed up on open decisions.
• Acted as a coordination point between client, vendor, and internal teams, streamlining technical communication and aligning decision-making.
• Analysed complex business processes and technical documentation, translating constraints into clear, actionable material for governance boards.
• Supported the definition and documentation of pairing/cyclic-grid, weekly and repairing optimisers, and GO interfaces at requirements and governance level.

Refinery naval scheduling · Python, AWS, AI/ML · Italy, hybrid

• Developed Python optimisation algorithms to model complex objective functions, forecast arrival times, and allocate resources across vessel scheduling and turnaround.
• Built a digital platform on AWS with a web interface for data management and visualisation, plus AI/ML and predictive models for logistics scenarios.
• Streamlined port schedules to reduce demurrage costs and protect continuity of biofuel production.
• Worked with logistics experts to turn operational constraints into model inputs and improve supply-chain resilience.

Accounting data · reporting

• Managed and processed accounting data for multiple client companies, ensuring accuracy and consistency across financial records.
• Identified and corrected discrepancies in financial statements, reducing reporting errors and improving data reliability.
• Collaborated with cross-functional teams to streamline accounting review processes and shorten reporting cycles.

Milan, Italy

Microsoft tools, Git, Power BI, Linux, Python, Java, HTML, Spring, Angular, SQL, public speaking, project management.

Building and repairing PCs since age 11; comfortable with laptops, desktops, servers, and the ugly part of the cable management.

• Raspberry Pi cluster running Kubernetes; main server wakes on demand via Wake-on-LAN.
• Ryzen 7 2700X, RTX 2060 for heavy GPU work, GTX 1050 for light work, 32 GB DDR4, 2× 2 TB HDD + 256 GB NVMe.
• Docker-based Plex scheduling that moves load from the 1050 to the 2060 when too many users are watching.
• TrueNAS, Jellyfin, Pi-hole/AdGuard; daily driver is a Lenovo LOQ (Ryzen 7 7435HS, RTX 4060 Mobile, 16 GB 6000 MHz, 1 TB NVMe).

Next.js, TypeScript, GSAP, WebGL, static export, Cloudflare. Case study →

Python, Discord IPC/RPC. GitHub ↗

Python, AWS, AI/ML, scheduling and resource allocation. Case study →