iJOBS Industry Insights: Data Science and AI at BMS

  • November 13, 2023
iJOBS Blog

By Jiawen Chen

In recent years, the popularity of data science and artificial intelligence (AI) has steadily increased within the pharmaceutical industry. As a result, many bench scientists are trying to venture into computational science. On September 7th, iJOBS invited Dr. Gregory Barker to share his insights on approaching drug discovery from a bench scientist to a data scientist. If you are interested in the transition, keep reading to find invaluable advice from Dr. Barker.


Who is Dr. Gregory Barker?

Dr. Gregory Barker is a Scientific Associate Director at Bristol Myers Squibb (BMS). He has been with BMS for around 12 years. Before joining BMS, he spent almost 3.5 years at Merck as a process engineer focusing mainly on optimizing and scaling up lipid nanoparticle formulations for RNA therapeutics. He is trained as a chemical and biomolecular engineer, having obtained his Ph.D. from the University of Pennsylvania.


How did he find avenues to his current role?

Dr. Barker's career path is a testament to his evolving role in the pharmaceutical industry, driven by an unwavering quest for knowledge, adaptability, and personal preference. His early engineering education during his undergraduate and graduate education laid the foundation for an eventual transition to data science. Dr. Barker obtained his bachelor’s degree in chemical engineering from the University of Delaware. At that time, he worked in Dr. A. M. Lenhoff’s lab, which focused on chromatography and other lab-based techniques with minimal coding usage. During his Ph.D., he transitioned to chemical and biomolecular engineering, where he extensively used MATLAB for data analyses, visualizations, and basic statistics, as well as more focus on drug discovery, RNA interference, and gene therapy. This transition during his Ph.D. set the stage for his deep dive into data science. Around 2008, Dr. Barker was hired by Merck due to his background and expertise in chromatography, process development, industrial statistics, and experimental design. Although he wasn’t a pure data scientist in the early stage of his career, his role at Merck gave him an excellent opportunity to evolve as a data scientist while learning the inner workings of large pharmaceutical companies, especially large-scale manufacturing. After a few years at Merck, he made a leap to BMS to work on high-throughput process development and chromatography. In this role, he used computational simulations and process optimization tools to develop a bridge between wet lab experiments and in silico modeling. From his experience, he emphasizes the importance of the interplay of in silico modeling and real-world experiments to speed up the drug discovery process. About four or five years ago, he made another shift to the drug discovery field due to his growing interest in exploring new opportunities in the drug development process.


What are his suggestions about the transition?

Dr. Barker's journey offers valuable insights into career transitions. His career shift from a traditional chemical engineering background to data-driven roles within the pharmaceutical industry exemplifies the importance of adaptability and a thirst for diverse experiences and new knowledge. At the end of the seminar, Dr. Barker's emphasis on exploring one's interests in career choices serves as a key takeaway - knowing what genuinely excites and motivates you can guide successful transitions and open doors to exciting and uncharted territories within the data-driven fields of today's pharmaceutical industry. He also mentioned that expertise from wet lab experiments is valuable to the pharmaceutical industry, although the demand for data scientists is rapidly increasing. He suggested applicants carefully read job descriptions when looking for jobs and highlight the significance of their background to companies and their strong interest in the jobs to attract recruiters’ attention. He also noted it would be valuable for everyone to gain experience in wet lab and data science roles.  To end the seminar, Dr. Barker pointed out the significance of enjoying jobs, collaborating with others, and being authentic.

These shared experiences of Dr. Barker are perfect for graduate students and postdoctoral researchers trying to transition from bench scientists to data scientists. Therefore, if you want to embark on a journey in computer science, don’t miss out on the chance to gain valuable information for your transition!

This article was edited by Senior Editor Shawn Rumrill and Senior Editor Sonal Gahlawat