Shelby Golden

Shelby Golden is a data scientist with a background in computational mathematics, molecular biology, and biochemistry. She holds a Master of Science in Applied Computational Mathematics from Johns Hopkins University and dual Bachelor of Science degrees in Molecular, Cellular, Developmental Biology and Biochemistry with a minor in Engineering in Applied Mathematics. Prior to joining Yale, she worked in industry, hospitals, and academia as a wet-lab research scientist, studying Chronic Obstructive Pulmonary Disease, Down Syndrome, vaccine development and efficacy testing, and CAR-T Cell Therapy across basic research, upstream development, and clinical research projects.

At Data Science and Data Equity, Shelby supports internal projects and consultations across YSPH and the broader Yale community. She creates workshops for the Coffee, Cookies, and Coding (C-Cubed) series, develops strategic projects such as the Resource Navigation Tool, and has served as an instructor and mentor for the Big Data Summer Immersion at Yale (BDSY) program. She also manages the group's GitHub repository and ensures compliance with data use agreements and IRBs.

Researchers can engage Shelby for support on analytical projects through all phases, from data exploration and model building to performance evaluation and implementation. Her consultations include user support and training in R and Python, AI/ML integration guidance, data cleaning and harmonization, R package development, and creating interactive dashboards using Shiny and Quarto. Her expertise in both computational sciences and biology allows her to effectively bridge the gap between data and domain knowledge.