I graduated from Northeastern University with a Bachelor of Science in Health Sciences, where I initially embarked on a career path in healthcare. My professional journey included roles as both a medical assistant and surgical assistant at BIDMC. It was during my capstone project, "Hearing Aid Access Among Geriatric Patients Research at Harvard," that I was introduced to the fascinating realm of data science. In this project, I meticulously analyzed the National Health and Aging Trends Study (NHATS) dataset to examine the correlation between hearing aid accessibility and factors such as income, education, and age. Employing regression analysis, data pattern exploration, and Stata for graphical visualizations, I compiled a policy memo advocating for Medicare policy changes to address disparities in hearing aid access across genders, education levels, and races.This pivotal experience prompted me to pivot my career, merging my passion for healthcare with data science. Seeking to strengthen my data skills, I embarked on an internship as a Data Engineer at Life Science Connect, where I gained invaluable insights into the field. Following this, I successfully completed a certificate program in Applied Data Science from MIT. In my capstone project, I constructed a Convolutional Neural Network (CNN) deep learning model for malaria detection using image processing, further solidifying my fascination not only with data science but also with artificial intelligence.Subsequently, I joined Odysseus Data Services as a Data Scientist, specializing in crafting and executing studies using R programming, with a primary focus on serving the pharmaceutical sector. Currently, I am furthering my expertise by pursuing a Master of Science in Health Data Science from Harvard T.H. Chan School of Public Health, where I am eager to explore the limitless possibilities at the intersection of healthcare and data science.