Abhilash Menon
About Abhilash Menon
Abhilash Menon is the Associate Director of Predictive Patient Solutions at Bristol Myers Squibb, specializing in applied machine learning and data science.
Company
Abhilash Menon currently works at Bristol Myers Squibb in the New York City Metropolitan Area. He holds the position of Associate Director, Predictive Patient Solutions, in the Commercialization Data Science team. Bristol Myers Squibb is a global biopharmaceutical company dedicated to discovering, developing, and delivering innovative medicines.
Title
As the Associate Director of Predictive Patient Solutions at Bristol Myers Squibb, Abhilash Menon leads initiatives in commercialization data science. His role involves leveraging predictive analytics to support data-driven decision-making and enhance patient outcomes in the biopharmaceutical industry.
Education and Expertise
Abhilash Menon has a robust educational background in computer science. He earned a Master of Science (MS) degree from Clemson University, specializing in computer science from 2016 to 2017. Before that, he completed his Bachelor of Engineering (B.E.) in computer science at Gujarat Technological University. His expertise lies in applied machine learning, A/B testing, data architecture, and ML Ops for people and consumer analytics.
Professional Background
Before joining Bristol Myers Squibb, Abhilash Menon gained substantial experience at IBM. He served as a Senior Data Scientist & Technical Lead from 2022 to 2023, an Advisory Data Scientist from 2020 to 2022, and a Data Scientist from 2018 to 2020, all in the Greater New York City Area. He also worked as a Graduate Research Assistant at Clemson University from 2016 to 2017.
Specialization and Skills
Abhilash Menon specializes in applied machine learning and has a strong record of delivering impactful projects across various industries. He is experienced in designing and conducting A/B tests, building data pipelines, and managing machine learning models. He is passionate about solving complex business problems through data-driven insights and has a keen interest in driving innovation in this field.