Nick Bachelder is a 2024 graduate from the Master of Biostatistics program at Duke University. Nick lives in Durham, NC, and works remotely for Biocore, LLC, as a Data Scientist.
Please tell us about the pathway to your current position. What led you to the job you now have?
As a graduate student at Duke, I was interested in applying data science and machine learning in the field of sports performance and analytics. During my first year in the Biostatistics program, I competed in a machine learning competition hosted by the NFL, which focused on developing metrics with tracking data associated with on-field player-performance. I was fortunate enough to be able to go present my work at the NFL combine, and after that I pursued job opportunities through the connections I made there.
Can you describe your current role and responsibilities?
I now work at Biocore, where I focus on leveraging advanced analytics and machine learning to optimize injury prevention strategies within the NFL's Digital Athlete Program. I spend most of my time developing predictive models that deliver insight on and contribute to proactive player health management. This includes different statistical methods I learned at Duke, from regressions on observational data to determine effects of various equipment, to maintaining larger deep learning models tracking how on-field movement correlated with various injury outcomes.
What interesting things have you learned about the field of biostatistics now that you are working in it?
One of the best things about applying biostatistics with real-world applications is that you are always learning. Regardless of what project I work on, being successful always requires developing a grasp of the discipline at hand. While the statistical tools you bring with you to each project are similar, most of the work is ensuring the assumptions and choices you make with those tools fit into the context of the problem, and that means always learning.
What do you most enjoy about your work?
I enjoy that data science work is multifaceted. Much of my job is very focused, as I spend at least half my time coding and thinking about new approaches to problems. I also spend almost half my time working collaboratively and communicating with my team, both presenting progress and getting new perspectives. This dichotomy of both social and focused work is unique to data science, and I enjoy it most.
How did Duke’s Master of Biostatistics program prepare you for your work or any challenges you face in your current role?
The Master of Biostatistics program was great because, in addition to the traditional class workload offered, it presented tons of opportunities beyond class to apply what I was learning. In addition to the research opportunities that writing a Master’s thesis opened for me, I also was able to explore capstone projects offered by Duke AI Health which drove my interest in deep learning, AI, and ML.
Were there any faculty members or mentors who had a significant impact on your learning journey?
The entirety of the staff and faculty at Duke were an amazing part of my graduate experience. At least academically, I was influenced largely by my thesis advisor Dr. Terry Hyslop, and my committee that included Dr. Nosayaba Osazuwa-Peter and Dr. Jesse Troy. Additionally, I was most interested in causal statistics because of the class taught by Dr. Laine Thomas.
What are some projects or achievements of which you’re particularly proud?
I am most proud of the work and publications I was fortunate enough to be a part of during my time in graduate school, which ranged in focus from identifying socioeconomic risks for breast cancer, studying pollen visitation of specialist bees, and examining how insulin resistance is linked to cancer risk in women.
What advice would you give to current or prospective students in the biostatistics program?
Explore opportunities to pursue research available to you at Duke and within the biostatistics program! They are just as valuable as the classes you take.
What are your goals for the next few years in your career?
I want to continue to work in data science and machine learning. I look forward to continuing to pursue my career in the field of sports analytics and performance.
How do you balance work and personal life while working in a demanding field?
I like to try to divide my day to separate periods of focused work, collaborative work, and time for me. I find having interests outside of work helps me to feel refreshed and excited to be productive. For me, I like to be active, cook good food, and play music, but that can look different for different folks!
Is there anything else you’d like to share with future MB students?
Enjoy the classes you take, and always pursue projects/opportunities that you are interested in.