Start a career in data science

Although genetics training is highly transferable, transitioning out of academia can present many challenges. We interviewed Tara Zeynep Baris to discuss her transition from college to a career in data science.

Baris earned his Ph.D. in evolutionary genomics at the University of Miami. Desiring flexibility and a diverse workload, Baris pursued data science through a postgraduate training opportunity with Insight Data Science.

Courtesy of Tara Zeynep Baris of HUB Ocean

She then moved on to a research and development role for Nielsen, an audience data analytics company for media 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 under the World Economic Forum.

She shared her experience working in the data science world and how her Ph.D. her background in genomics prepared her for this career path.

How did you decide to leave academia?

It was not an easy decision. I love research and having the freedom to explore something all the way. 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 need to follow vacancies and become experts in a research field. Of course, you can always learn new things and change a little, but there’s not a lot of flexibility because you won’t get a full-time position in a research area that’s completely different from your background.

What were the biggest challenges of the transition to industry?

In academia, especially as a Ph.D. student, you are in a learning situation. So when you make mistakes, you are generally not held responsible for the financial implications. You just move on and learn. This is not always the case when you work for a company. You could potentially cost your business a major client or contract. In the industry, you will more frequently feel the pressure to do things right without as 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 angles, and then drawing conclusions. In the industry, you don’t have time to get that level of depth on every project. It was a bit difficult for me because I used to be completely immersed in what I was looking for, but that’s not necessarily what the industry needs. Many times I have only scratched the surface before a project is complete.

The other major challenge is the interview process in the industry, which was a whole new world for me. For a doctorate. or a postdoctoral position, you can give a talk and then meet professors and have casual conversations about research.

Data science interviews require insane preparation. I was interviewed and challenged to demonstrate my coding and data science specific skills through separate specialist interviews. It was a stressful process and took more time to prepare for each round of technical interviews.

What are your daily responsibilities?

Our team is focused on creating a platform that makes it easy for different types of users to access the data they need to create a more sustainable ocean, whether they are industry professionals , decision-makers or researchers.

I take on different roles within the team. First, I do a lot of user interviews and talk to people who will use our product to make sure it meets their needs. Second, I work to understand what data exists, in what formats it exists, and how we can make it more accessible to people. This involves working with different types of databases, including geospatial datasets, and then determining what can actually be done with the data.

I read articles to understand why certain data is useful. Sometimes that means working with our partners at different research institutes and universities in Norway to understand the downstream value of that data and coding different functions or working on different models that help people use that data.

Having a background in research is really helpful in these cases, especially because people in research and industry have very different ways of communicating. It is sometimes easier for me to communicate with researchers because I understand their language and what is important to them. For example, I’m collaborating with the University of Tromsø on an environmental DNA project, which draws heavily on my background in genomics.

How does your training compare to that of other data scientists in the industry?

When I started my first job in data science at Nielsen (a television rating company), almost everyone on the team had a PhD. in physics, biology or even fishing. It was quite a large 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 marine data, but is not strictly from research.

At first, it wasn’t an easy transition for me into the industry, but I had team members who were really supportive and helped me bridge the gap between academic training and what which is necessary in a position in the industry.

What do you like most about your job?

I like the fact that I do a lot of different things. It’s important to me personally, because I don’t like to do a single thing repetitively. It’s nice that sometimes I spend my days talking to people and other days I focus on coding. I also contribute to overall ideas on the direction of our product. So my favorite thing is that I get my hands on a bit of everything, and I can connect with people on other parts of the project because we’re such a small team. For example, I like to try to understand what data engineers do, learn from them and contribute to their work.

Has your position in a policy-oriented organization improved your communication skills?

I give a lot of presentations that include discussions on technical topics to non-technical people with a very wide range of expertise. I also present to different industries or government organizations or universities, who are interested in partnering with us. This includes making them understand exactly what we do and where we stand. For this, each presentation must be adapted to the listener, so I spend a lot of time fine-tuning presentations and rarely give the same speech twice.

I struggled a bit at first because I’m so used to science talks, where I present all the evidence I’ve gathered, then show how I come to a conclusion after turning over each stone.

In my current position, the small details are not always so relevant. At first, I may have been giving too much information because I was worried that I didn’t have enough data to support my conclusions. Now I have learned what is really important and focus my discussions more closely.

What advice do you have for someone considering a move into data science?

First, it’s important to understand what motivates you as a person to make sure you’re following a career path with opportunities that will make you happy. Second, be patient. It is really difficult to make the transition to a new career and a new environment. This does not happen overnight, but the skills you acquire during your doctorate. will be useful. Staying determined and continuing to work at it are key.

Finally, create a support network of people who have the careers you want during your transition. I always reach out to people who have walked the same path and understand their experience and the obstacles they have overcome. They can impart knowledge that will make your job easier or even provide resources that you might not have thought of.

This article first appeared in Genes to Genomes, a blog of the Genetics Society of America. Read the original article here.

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