Although training in genetics is highly transferable, transitioning out of academia can present a lot of challenges. We interviewed Tara Zeynep Baris to discuss her transition out of academia and into a career in data science.
Baris received her Ph.D. in evolutionary genomics at the University of Miami. Desiring flexibility and a diverse workload, Baris pursued data science through a postdoctoral training opportunity with Insight Data Science.
Courtesy of Tara Zeynep Baris of HUB Ocean
She then transitioned to a position in research and development for Nielsen, an audience data analytics company for media platforms.
Baris is currently a senior data scientist with HUB Ocean (formerly the Centre for the Fourth Industrial Revolution–Ocean, or C4IR Ocean), which operates under the World Economic Forum.
She shared her experience working in the world of data science and how her Ph.D. training in genomics prepared her for this career path.
How did you decide to transition out of academia?
It wasn’t an easy decision. I love research and having the freedom to explore something to the end. However, I wanted flexibility in where I would live and the ability to try different opportunities until I found the right fit. In contrast, most academics have to follow open positions and become experts in one research domain. Of course you can always learn new things and change slightly, but there isn’t a ton of flexibility as you won’t get a full-time position in an area of research that is completely different from your background.
What were the biggest challenges in transitioning to industry?
In academia, especially as a Ph.D. student, you’re in a learning position. So, when you make mistakes, you aren’t usually held responsible for the financial implications. You just move on and learn. This isn’t always the case when you work for a company. You could potentially cost your company an important client or contract. In industry, you will more frequently feel the pressure to get things right without as much space to learn by making mistakes.
Another difference is in the depth of projects. In research, you have the freedom to explore a topic by reading everything in the literature, looking at the data from different perspectives, and then making conclusions. In industry, you don’t have the time to get that level of depth on every project. This was a bit difficult for me because I was used to being completely immersed in what I was researching, but that’s not necessarily what’s needed in industry. Many times I’ve just scratched the surface before a project ends.
The other main challenge is the interviewing process in industry, which was a whole new world to me. For a Ph.D. or a postdoc position, you might give a talk, then meet with faculty and have some laid-back conversations about research.
Data science interviews require an insane amount of preparation. I was quizzed and challenged to demonstrate competency in coding and data science–specific skills through separate specialized interviews. This was a stressful process and took more time to prepare for each set of technical interviews.
What are your day-to-day responsibilities?
Our team’s focus is on building a platform that makes it easy for different types of users to get access to the data that they need to create a more sustainable ocean, whether that’s …….
Source: https://www.asbmb.org/asbmb-today/industry/060722/starting-a-career-in-data-science