Since I got my Master’s Degree in Computational Physics in 2013 I have focused heavily on doing science and developing software for that purpose. During these last 10 years of doing science and software (at first for my scientific research during my Ph.D., then in the industry) I have been able to learn a lot about the interaction between data science and machine learning, favoring and building (both from the DS and the MLE side) solutions that are scalable and democratic: allowing all of the Data Scientist to do the experiments and exploration they want, easing considerably the learning curve to put things into production.

I have developed several experiment protocols and machine learning solutions, both from statistics and software engineering/design, while technically leading high-performance teams. Because of my background in fundamental science, I am driven to build knowledge-centric cultures, where knowledge extends both in space (allowing more people to know about the system we are solving) and in time (deepening the organization’s knowledge of each specific subject in each iteration).