Sarah Shi is an accomplished professional with a diverse background in data science, quantitative risk modeling, and machine learning. Experience includes roles as a Quantitative Risk Modeling Summer Analyst at BNY Mellon, where Sarah focused on credit risk modeling and forecasting, and a Data Science Intern at Spark451 Inc., optimizing marketing efficiency and designing data processes. As a Vice President at ML Engineer | AI Hub, responsibilities encompassed rule extraction and generative AI applications. In the role of Data Scientist at Advanced Digital Solutions, Sarah employed time series models and clustering algorithms for financial predictions. Additional experience includes a teaching assistant position at New York University and a research assistant role at UC Davis, as well as a position as a Commodity Data Analyst at McKeany-Flavell Company, Inc. Currently, Sarah is a Software Engineer in Machine Learning at Google, specializing in personalized Gen-AI agents. Sarah holds a Master's degree from New York University and a Bachelor of Science in Statistics from the University of California, Davis.
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