Leonardo Portes is a data scientist and researcher with a PhD in advanced time-series analysis and nearly a decade of industry-focused experience applying machine learning and complex-systems theory to energy, resources and health. Based in Western Australia under a Global Talent Visa, he has led and delivered scalable, interpretable decision-support tools and 3D/4D visualisation dashboards for clients including Woodside, Anglo American, Rio Tinto and CSIRO. He blends rigorous academic research—publishing in outlets such as Nature’s Scientific Reports and Physical Review E—with practical engineering, optimising algorithms (e.g., speeding recurrence analysis from hours to seconds) and developing patented monitoring technology. Comfortable mentoring and building open-source solutions, he contributes reproducible Python tooling that aligns with Scikit-learn/Statsmodels APIs and has spearheaded pipelines that classified millions of 3D geological models. He prefers transparent, principle-driven solutions over needless complexity and is adept at translating complex science into usable workflows for cross-disciplinary teams.
9 years of coding experience
7 years of employment as a software developer
PhD Advanced time series analysis of human motor behavior, PhD Advanced time series analysis of human motor behavior at Universidade Federal de Minas Gerais
Contributions:4 PRs, 4 pushes, 5 branches in 1 day
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Leonardo Portes - Data Scientist, Digital Orebody Knowledge