Fernando Trevisan

• Maintain and scale both the ML deployment pipeline and the Feature Store, ensuring high availability, observability, and flexibility for business demands. • Boost engineering team efficiency by creating and implementing frameworks, standards, and best practices for cloud automation. • Translate business needs into viable and reusable technological solutions, fostering a data-driven culture. • Deployment of machine learning models for the industrial, supply chain and R&D areas in data products.

Data Science Division (DataLab): • Development, maintenance, and monitoring of Python web application with near real time data pipeline; • Near real time E.T.L. maintenance; • Google Cloud Platform tools (cloudSQL, compute engine, BigQuery); • Data modeling;

Operation and logistics real time monitoring division. • Real-time data modeling; • Creating dashboards using Grafana to monitor operations and service level agreements; • Data analysis identifying improvement opportunities in our services; • Support to the logistics team with data extraction/visualization and automating reports.

Responsible for the company's reputation in the marketplaces. • Be the bridge between the business and the technology teams, describing users' main struggles; • Data analysis, reports, creating, and monitoring SLAs; • Creating dashboards using BI tools to monitor areas and orders; • Process automation via Python scripts; • Decision making and process changing; • Direct contact with marketplaces.

One year exchange program to study Mechanical Engineering. Modules: • BEng, Design A (product design with processes and material selection); • BEng, Powertrain and Vehicle Engineering; • BEng, CFD & FEA; • MEng, Biofuels and Combustion; • MEng, Micro and Nano Technologies; • German I;