Isha Verma
About Isha Verma
Isha Verma is a Senior Machine Learning Scientist specializing in small molecule drug discovery at Bristol Myers Squibb in Redwood City, California. She has extensive experience in data science and machine learning, having previously worked at Merck and published research in the field.
Current Title and Role
Isha Verma is currently a Senior Machine Learning Scientist specializing in small molecule drug discovery at Bristol Myers Squibb. She is based in Redwood City, California, United States, where she leverages machine learning techniques to advance pharmaceutical research.
Career at Bristol Myers Squibb
Isha Verma joined Bristol Myers Squibb as a Machine Learning Scientist in 2021. After a year in this position, she was promoted to Senior Machine Learning Scientist in the same specialized field of small molecule drug discovery. She continues to work in Redwood City, California, contributing to significant advancements in the field through her expertise in machine learning.
Previous Experience at Merck
Isha has extensive experience with Merck, holding various positions from Associate Data Scientist to Senior Data Scientist between 2015 and 2021. Her roles included stints in Branchburg, NJ, Rahway, NJ, South San Francisco, California, Palo Alto, California, and Prague, Czech Republic. At Merck, she was recognized for her innovation in data science with the 'Excellence in Innovation' award in 2020.
Educational Background
Isha Verma holds a Master of Science in Computer Science from the University of California, Los Angeles, completed between 2019 and 2021. She also earned a Bachelor of Science in Computational Biomedical Engineering from The University of Texas at Austin, where she studied from 2011 to 2015.
Research and Publications
Isha has published research focusing on the application of machine learning in small molecule drug discovery. She presented notable work at the International Conference on Machine Learning (ICML) in 2021, where she discussed a novel algorithm she developed, which improved the accuracy of drug-target interaction predictions by 15%.