I’ve been passionate about problem solving since a young age. The necessity of understanding problems in depth is necessary to implement code-based solutions, which sparked my interest in it as soon as I entered my engineering carreer. My interests are anything related to full-stack data science, model deployment and prescriptive analytics.

I graduated my MSc in Data Science in August 2022 and I’m currently working in developing Operations Research Tools Applied to Scheduling in Observatory Operations Project. With the aim to connect machine learning methods and operations research to generate valuable solutions.

Additionally, I have four years of experience in various Data Science projects such as coding and comparing different Machine Learning techniques for a multi-label lung cancer patient classification, developing Machine Learning code based on Natural Language Processing techniques for a multi-label complaint classification, developing strategies for the use of digital technologies to strengthen the decentralization of the management of the Regional Governments of Chile, improving the prediction of normal and abnormal pregnancies through a logistic regression when covariates are random effects from a nonlinear mixed model and building a server scheduler for ALMA Observatory. Previously, he helped to advise the State Bank of Chile through the application of various Machine Learning methods and techniques related to their customer churn and product offerings. He has also developed various end-to-end projects that range from data ingestion to value generation through AI models in different industries such as healthcare, astronomy, banking, and retail.