Leonardo Ramirez-lopez
Head Of Data Science
Switzerland
Summary
👤
Senior🎓
Top SchoolLeonardo Ramirez-lopez is Head of Data Science at BÜCHI Labortechnik AG, where he leads a team building operational, open-source sensing and spectral-analysis tools for environmental and agricultural applications. With over a decade of experience bridging chemometrics, NIR spectroscopy, and geoscience, he has translated academic research on soils and hydropedology into scalable products that support evidence-based resource management. He progressed from NIR software specialist to department head, combining hands-on algorithm development with product delivery and team leadership. Currently pursuing an MSc in Business Analytics at Imperial College London, he uniquely pairs rigorous geoscience training (Dr. rer. nat., magna cum laude) with modern data-product and business analytics skills. Colleagues know him for shipping reliable, user-friendly tools quickly and for a quirky fondness for "infrared" that reflects his domain focus.
10 years of coding experience
11 years of employment as a software developer
Master of Science - MS, Business Analytics @ Imperial Business School, Master of Science - MS, Business Analytics @ Imperial Business School at Imperial College LondonDoctor of Science, Geoscience, Doctor of Science, Geoscience at University of TübingenMaster's degree, Soil Sciences, Master's degree, Soil Sciences at USP - Universidade de São Paulo
Github Skills (11)
machine-learning10
resample10
sampling10
calibration10
preprocessing10
r-package10
signal10
cheminformatics10
infrared10
r10
inference1
Programming languages (2)
Github contributions (5)
l-ramirez-lopez/prospectr
Mar 2020 - Jan 2023
Contributions:560 commits, 29 pushes, 2 issues in 2 years 11 months
l-ramirez-lopez/resemble
Nov 2015 - Sep 2022
resemble is an R package for similarity-based modelling and local learning in spectroscopy. It provides tools for dissimilarity computation, nearest-neighbour search, memory-based learning, and spectral library optimisation (methods designed for large, heterogeneous spectral datasets where global models underperform)
Contributions:18 releases, 3 reviews, 680 commits in 6 years 11 months
rr-packagechemometricsspectroscopysoil-spectroscopy
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