Summary
Jeronimo Barois is a Machine Learning Engineer based in Berlin with nine years of experience applying data science, deep learning and distributed systems to problems in remote sensing, GIS, NLP, computer vision, ecology and ESG investing. He combines a strong mathematical and computational background from ITAM and an exchange at ANU with hands-on experience building production ML and cloud architectures for agile delivery. His work spans roles from data engineering and consultancy to ML deployments at firms like Blue Orange Digital, Experian and Earth, and earlier projects include UAV autonomy and recommender systems for OpenBlender. He has applied ML to ESG measurement in an industry research internship and built practical tooling such as K-nearest recommender notebooks, reflecting a pragmatic focus on reproducible workflows. Comfortable across research and operational contexts, he brings a distributed-systems mindset to model deployment and data pipelines. Colleagues can expect a blend of rigorous quantitative thinking with practical engineering that targets measurable impact in environmental and investment domains.
8 years of coding experience
1 year of employment as a software developer
Australian National University
Licenciatura Matematicas aplicadas, Licenciatura Matematicas aplicadas at Instituto Tecnológico Autónomo de México