She is passionate about creating data-woven stories and applying her skills to real-world problems. She has three years of experience as a data scientist, where she worked on projects for Top Fortune 500 clients in FMCG and healthcare sectors, using predictive analytics, time series forecasting, and demand forecasting. She also worked overseas with a financial group and a bank, creating business intelligence reports and coordinating in a cross-cultural environment. She is proficient in ML, NLP, Python, R, Cloud, and Git, and has done research on sentiment analysis and consumer behavior during COVID-19. She is a problem-solver, adept at simplifying complex procedures and working under intense timeline pressure. She has participated in competitions like MIT COVID-19 Hack and MIT Grand Hack Medicine, where she collaborated with multidisciplinary teams to develop innovative solutions for the pandemic and healthcare challenges.