Chiara Lepore is a Weather Quantitative Researcher with nearly two decades of expertise translating climate and geophysical science into production-ready machine learning and big-data pipelines. She has led end-to-end projects from research to scalable cloud deployments—cutting CMIP6 and operational forecast processing from weeks to days and reducing daily costs tenfold—using the Pangeo stack, AWS, Docker, and ARCO data formats. Her background spans academia (Lamont-Doherty, MIT) and industry (Gro Intelligence, Squarepoint), where she builds harmonized, bias-adjusted datasets and probabilistic models for severe weather, landslides, and other hazards. A hands-on software developer and mentor, she pairs deep knowledge of reanalysis and forecast systems (ERA5, GFS/GEFS, NMME, CMIP6) with practical data-engineering solutions for terabyte-scale analyses. Notably, she developed a high-performance Python package for global convective parameter computations, reflecting a penchant for building tools that speed science at scale. Based in New York, she bridges rigorous academic research and operational climate risk modeling for stakeholders across insurance and finance.
9 years of coding experience
15 years of employment as a software developer
phD Civil and Environmental Engineering hydrology, phD Civil and Environmental Engineering hydrology at Università degli Studi di Salerno
visiting scholar hydrology, visiting scholar hydrology at Massachusetts Institute of Technology
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