GSBS Alumni Profile: Ryon Graf, Ph.D.

By: Nicole Villa, M A

OETIS had the pleasure of hosting two career exploration workshops with our very own GSBS graduate student alumni, Dr. Ryon Graf. Dr. Graf graciously came to the Institute at the end of last year and spoke to our trainees about his career in clinical research at one of OETIS’s Careers & Coffee workshops. This career exploration exploration workshop series is designed to give trainees the opportunity to hear from professionals in various non-faculty career paths and discuss how they leveraged their education and training to transition into their positions. Dr. Graf gave such an amazing presentation that for the first time in the 7-year history of this series, trainees requested a follow-up session. Get to know more about our amazing GSBS alumni and his work in clinical research:

Q: What are your primary responsibilities as Director of Clinical Development?

A: I am responsible for turning standard of care clinical practice (real-world) data into insights that can be used to aid drug development and sometimes directly help patients. I am responsible for the strategies of how to do so, project planning, relationship management with key opinion leader physicians in the field and managing a team of data scientists.

Q: What do you find the most rewarding about your job/work? What do you find the most challenging?

A: Most rewarding: being able to work on things with immediate and tangible positive effects for cancer patients. Most challenging: the pace of industry in general. In academia, I was starved for data. In industry, we drown in data, and time becomes front and center as the most valuable resource.

Q: Describe your education and training which helped you get to this point in your career? How did you get your first job?

A: I’ve described this journey in a blog post! https://www.ryongraf.com/post/three-key-piecesof-career-advice

Q: What advice do you have for graduate students and postdocs interested in pursuing this career path?

A: Demonstrate that you can do rigorous science. Know what excites you. Learn and get opportunities to practice basic data science skills (coding): data wrangling, statistical inference, and data visualization. Also, read the aforementioned blog post!