I’m a machine learning engineer and applied statistician with 13 years of interdisciplinary experience applying advanced quantitative methods to solve real-world “wicked” problems, including 15 years of experience working on climate change and more than a decade in the field of illicit finance. I specialize in building rigorous, end-to-end AI systems for high-impact domains including climate finance, ESG, and financial crime. With a PhD in environmental science and a master’s in statistics, I bring both technical rigor and domain fluency to complex, cross-cutting challenges, drawing on a background in computational social science, political economy, and quantitative modeling. My work has contributed to real-world policy at the United Nations and produced research that uncovers hidden financial flows and supports data-driven sustainability. I’m currently seeking technical roles at the intersection of AI, climate, and finance, where I can apply advanced machine learning to tackle hard, messy problems.

I’m a full-stack developer: I like building things that work, and shipping them.




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