Although training in genetics is highly transferable, transitioning out of academia can present many challenges. We interviewed Tara Zeynep Baris to discuss her transition from academia to a career in data science.
Baris holds a PhD. in Evolutionary Genomics from 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 HUB Ocean’s Tara Zeynep Baris
She then moved on to a position in research and development for Nielsen, an audience analytics company for multimedia platforms.
Baris is currently a senior data scientist at HUB Ocean (formerly the Center for the Fourth Industrial Revolution – Ocean, or C4IR Ocean), which operates within the World Economic Forum.
He shared his work experience in the world of data science and as his Ph.D. her training in genomics prepared her for this career path.
How did you decide to leave academia?
It wasn’t an easy decision. I love research and 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. Conversely, most academics must pursue open positions and become experts in a research domain. Of course, you can always learn new things and change slightly, but there isn’t much flexibility as you won’t get a full-time position in an entirely different research area than your background.
What were the biggest challenges in moving to industry?
In academia, especially as a PhD. student, you are in a learning position. So when you make mistakes, you are usually not held accountable for the financial implications. You just have to go ahead and learn. This is not always the case when you work for a company. You could potentially cost your company a major customer or contract. In industry, you will more frequently feel the pressure to do things right without much room to learn by making mistakes.
Another difference lies in the depth of the 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 drawing conclusions. In industry, you don’t have the time to get that level of depth on every project. It was a little difficult for me as I was used to being completely immersed in what I was researching, but that’s not necessarily what is needed in the industry. Many times I have just scratched the surface before a project was finished.
The other main challenge is the interview process in the industry, which was a whole new world for me. For a PhD or postdoc position, you could give a speech, then meet the faculty and have some relaxed conversations about research.
Data science interviews require an insane amount of preparation. I was questioned and challenged to demonstrate coding proficiency and data science specific skills through separate specialist interviews. This was a stressful process and took longer to prepare for each series of technical interviews.
What are your daily responsibilities?
Our team’s goal is to create a platform that makes it easy for different types of users to access the data they need to create a more sustainable ocean, be it industry professionals, policy makers or researchers.
I take on different roles on the team. First, I do a lot of user interviews and talk to the people who will be using our product to make sure it meets their needs. Second, I work to understand what data is available, in what formats it exists, and determine how we can make it more accessible to people. This involves working with different types of databases, including geospatial datasets, and then calculating what can actually be done with the data.
I have read documents to understand why certain data is useful. Sometimes this involves working with our partners at different research institutes and universities in Norway to understand the downstream value of that data and code different functions or work on different models that help people use that data.
Having a research background is really helpful in these cases, especially as people from research and industry have very different ways of communicating. Sometimes it is easier for me to communicate with researchers because I understand their language and what is important to them. For example, I am collaborating with the University of Tromso on an environmental DNA project, which has drawn heavily from my background in genomics.
How does your training compare to that of other data scientists in the sector?
When I started my first data science position at Nielsen (a TV rating firm) almost all of the team members had PhDs. in physics, biology or even in fisheries. It was a pretty big team compared to where I am now, which has two data scientists and a few consultants. The other data scientist I work with now has a background in maritime data but does not come from a strictly research background.
It wasn’t an easy transition for me in the industry at first, but I have had really supportive team members who have helped me bridge the gap between academic training and what is needed in an industry position.
What do you like most about your position?
I love that I do a lot of different things. This is important to me personally, because I don’t like doing just one thing repetitively. It’s nice that sometimes I spend my days talking to people and other times I focus on programming. I also contribute to general ideas about the direction of our product. So, my favorite thing is that I have my hands in a little bit of everything and I can be in contact with people in other parts of the project because we are such a small team. For example, I like trying to understand what data engineers are doing, learning from them and contributing to their work.
Has your position within a policy-based organization improved your communication skills?
I give a lot of presentations which include talking about technical things to non-technical people with a wide range of skills. I also introduce myself to various industries or government organizations or universities, who are interested in partnering with us. This includes letting them know exactly what we do and where we fit in. For this, each presentation must be tailored to whoever is listening, so I spend a lot of time editing presentations and rarely do the same speech twice.
I struggled a bit at first because I am so used to scientific talk, where I present all the evidence I have gathered and then show how I came to a conclusion after turning each stone.
In my current position, the small details aren’t always that relevant. In the beginning, I was perhaps providing too much information, because I was afraid that I did not have enough data to support my conclusions. Now, I have learned what is really important and have focused my talks more narrowly.
What advice do you have for someone who is considering transitioning to data science?
First, it is important to understand what makes you tick as a person to be sure you are following a career path with opportunities that will make you happy. Second, be patient. It is really difficult to switch to a new career and a new environment. It doesn’t happen overnight, but the skills you acquire during your PhD. it will be useful. Staying determined and continuing to work on it is essential.
Finally, build a support network of people who have the career you want while transitioning. I always reach out to people who have traveled the same path and understand their experience and the obstacles they have overcome. They can impart knowledge that will make things easier for you or even provide you with resources you might not have thought of.
This article first appeared on Genes to Genomes, a blog of the Genetics Society of America. Read the original article here.