Oceanographer and Climate Scientist turned data engineer with 10+ years experience working with climate models across a broad spectrum of applications. My background includes: – Writing scalable, open-source python software for assessing the skill of subseasonal-to-decadal weather/climate forecasts (https://climpred.readthedocs.io/en/stable/) – Running global high-resolution ocean models on HPC clusters with 10,000 CPU cores, producing many TBs of novel output – Writing parallel python code with dask and xarray to answer questions related to ocean biogeochemistry, terrestrial carbon, and physical climate risk – Building a data platform for a commercial climate science team using AWS, Terraform, and Kedro to support efficient analysis of 100s of TBs of climate data – Publishing 10 peer-reviewed, academic articles related to oceanography, climate science, and open-source softwareMy work as a data scientist at McKinsey focused on developing and maintaining python code to project physical climate hazards such as agricultural drought, urban water stress, heat stress, landslides, extreme winds, and extreme precipitation. As a software engineer, I maintain our statistical downscaling system, build out new software for climate hazards, and manage our AWS cloud storage and computing.I earned my PhD in Atmospheric and Oceanic Sciences at the University of Colorado Boulder, where I studied carbonate chemistry in oceanic upwelling systems. My studies span a diverse range of applications, but are united by their use of state-of-the-art climate modeling and high-performance computing to address key questions about the impacts of internal climate variability and climate change on coastal oceans and the Southern Ocean.