About me

I'm a Civil and Water Resources Engineer with an MSc from KU Leuven & Vrije Universiteit Brussel (cum laude, VLIR-UOS scholarship), building on a BSc in Civil Engineering from Universidad Mayor de San Simón, where I worked as a research assistant in the Hydraulics Laboratory.

My work sits at the intersection of hydrology, geospatial data engineering, and analysis. I've built automated pipelines for satellite and reanalysis climate data, calibrated and validated hydrological models against observed streamflow, and developed Python applications for statistical downscaling of climate projections. My focus has expanded into data engineering and machine learning: cloud-based data pipelines (Microsoft Fabric, Azure ML), and I'm strengthening my software engineering practice with testing (pytest), containerization (Docker) and continuous integration.

I completed a Postgraduate Diploma in Machine Learning at Universidad Católica Boliviana, covering statistics, regression and classification, and MLOps on Azure. I care about building scientific tools that are not just correct, but tested, reproducible, and usable by others, treating validation as part of the software, not an afterthought. I am committed to fostering collaboration through open-source contributions on GitHub.

Last but not least, I participated in various volunteer programs, such as: Tandem , Climatematch , and ONU Volunteers . These experiences not only refine my personal skills, but also serve as a platform for expanding my professional network, reinforcing my commitment to making meaningful contributions in both my career and the broader community.