Juliane Manitz is a PhD-trained statistician and fractional data scientist focused on translating advanced analytics into climate solutions, with 15+ years applying machine learning, network analysis, and visualization across academia, biotech, and climate tech. She has led statistical strategy for late-stage clinical trials and built ML frameworks and open tools in regulated environments, then pivoted to mission-driven market intelligence and spatial analysis for food-waste recovery and climate initiatives. Her 25+ publications (12 as first author) and 700+ citations reflect a knack for cross-disciplinary problems from genetics to epidemiology and complex networks. A recognized climate justice leader in Massachusetts, she pairs rigorous methodological thinking with grassroots organizing experience and recent Fellowship training to accelerate impact in the climate sector. Notably, she combines deep domain learning speed with hands-on engineering—R, SQL, APIs and GIS—to move prototypes into operational decisions.
10 years of coding experience
6 years of employment as a software developer
Ludwig Maximilian University of Munich
Doctor of Philosophy - PhD, MATHEMATICS AND STATISTICS, Doctor of Philosophy - PhD, MATHEMATICS AND STATISTICS at The University of Göttingen
R package: sampling procedures from the book 'Stichproben - Methoden und praktische Umsetzung mit R' by Goeran Kauermann and Helmut Kuechenhoff (2010).
Contributions:40 commits, 33 pushes, 1 branch in 1 year 3 months
R package NetOrigin: Origin Estimation for Propagation Processes on Complex Networks
Contributions:15 reviews, 2 PRs, 31 pushes in 4 years 2 months
estimationr-packagepropagationcomplex-networks
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