Laura Jacoby is a Senior Data Scientist based in Seattle with six years of experience applying statistical and machine learning methods to scientific and business problems. She has a strong track record building and deploying regression and ensemble models (Random Forest, Kernel Ridge) and creating predictive frameworks that saved scientists millions of computational hours while at Argonne National Laboratory. Comfortable bridging research and production, she led development of shared Python tooling and GitHub-based workflows at the University of Washington that cut data workup time by 60% and trained non-technical users. Her background in biochemistry and inorganic chemistry with a data science focus enables her to translate domain knowledge into robust data solutions across academia, national labs, startups, and industry (now Nielsen). She brings experience in Bayesian hyperparameter tuning, PCA/KNN data cleaning, test-driven development, and scientific publishing, and has been recognized with fellowships and research awards. Colleagues rely on her blend of experimental rigor and pragmatic engineering to turn complex datasets into usable insights.
6 years of coding experience
8 years of employment as a software developer
Master of Science - MS, Inorganic Chemistry with Data Science Option, 3.73, Master of Science - MS, Inorganic Chemistry with Data Science Option, 3.73 at University of Washington
Bachelor of Science (B.S.), Biochemistry, Bachelor of Science (B.S.), Biochemistry at University of California, Santa Barbara
Bachelor of Science (BS), Biochemistry, 3.74, Bachelor of Science (BS), Biochemistry, 3.74 at University of California, Los Angeles
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