Leveraging Data Science to improve car's quality & automating day to day tasks.• Analyzing Root Cause Analysis in high pressure Die Casting Machines for car FUB & RUB.• Anomaly detection & deduplication of Paint defects.• Computer vision microscopic cell contamination detection.

• Developed a powerful tool used by the whole factory to inspect the yield of casting process.• Developed a powerful tool to check the quality of cars with dynamic real-world tests.• Created a pipeline to standardize & store the data of all welding robots of the factory.• Responsible of various big code refactoring tasks in different projects & kickstarter of the clean code standards of the team.• Performed specific investigations on injection data of die casting process to recognize patternsthat generate defects.• Gave internal talks regarding clean code & efficient pandas usage.

Selected for the competitive Summer Student Programme, 12 weeks contract.Development of k8sUpdater, a Kubernetes operator that checks for newer versions of deployment's images at Gitlab and DockerHub, and updates automatically or notifies the user, depending on its preferences.- Python, mainly kopf, kubernetes, requests, gitlab and packaging.- Docker, Kubernetes- Git, GitHub, Mattermost

Development of a huge time series historics database, cleaning and extracting features from live csv files and past raw data. Agile methodologies, 100% remote, english as the primary language for communication.- Python, mainly pandas, numpy, boto3, psycopg2, multiprocessing.- PostgreSQL, AWS S3, PgAdmin, Docker, Gitlab.- Jira, Factorial, Slack, Confluence.

I worked remotely on a big data project as part of a team, specifically in the research and development area, analyzing and modeling data to predict how successful each product would be, in order to pick the right time to deliver each, coding documented and scalable code.- Python, mainly: pandas, sklearn, matplotlib, seaborn, numpy, argparse.- Linux Mint OS with Vim.- Google Cloud with tmux and bash scripts.