Starting a career in data science

Although teaching genetics is highly portable, the transition from academia can be very challenging. We interviewed Tara Zeynep Baris to discuss her transition from academia to a career in data science.

Barris received his doctorate. in Evolutionary Genomics at the University of Miami. Desiring flexibility and a variety of workloads, Barris pursues data science with the opportunity for postdoctoral training with Insight Data Science.

Courtesy of Tara Zeynep Baris of HUB Ocean

She then moved on to a research and development position at Nielsen, an audience data analysis company for media platforms.

Barris is currently a senior data scientist at HUB Ocean (formerly the Center for the Fourth Industrial Revolution – Ocean, or C4IR Ocean), working for the World Economic Forum.

She shared her experience from working in the world of data science and how her doctor. genomics training prepared her for this career.

How did you decide to leave academia?

It was not 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 opportunity to try different options while finding the right one. In contrast, most scientists need to follow open positions and become experts in a research field. Of course, you can always learn new things and change slightly, but there is not much flexibility, as you will not get a full-time position in a field of research that is completely different from your background.

What were the biggest challenges in the transition to the industry?

In academia, especially as a doctorate. student, you are in a position to study. So when you make mistakes, you are usually not responsible for the financial consequences. You just keep going and learning. This is not always the case when you work for a company. You could potentially cost your company an important customer or contract. In the industry, you will more often be pressured to fix things without so much room for learning by making mistakes.

Another difference is in the depth of the projects. In research, you have the freedom to research a topic by reading everything in the literature, looking at data from different perspectives, and then drawing conclusions. In the industry, you don’t have time to get that level of depth for every project. It was a little difficult for me because I was used to being completely immersed in what I was researching, but it’s not necessarily necessary in the industry. Many times I just scratched the surface before the project ended.

The other main challenge is the process of interviewing in the industry, which was a whole new world for me. For a doctorate or postdoctoral position, you can talk, then meet with the lecturers and have some quiet conversations about research.

Data science interviews require insane preparation. I was asked and challenged to demonstrate coding competence and data science-specific skills through separate specialized interviews. It was a stressful process and took more time to prepare for each set of technical interviews.

What are your daily responsibilities?

Our team’s focus is on building a platform that makes it easier for different types of users to access the data they need to create a more sustainable ocean, whether they are industry professionals, politicians or researchers.

I take on several different roles in the team. First, I do a lot of interviews with users and talk to people who will use our product to make sure it meets their needs. Secondly, I work to find out what data is available, in what formats it is and to determine how we can make it more accessible to people. This involves working with different types of databases, including geospatial datasets, and then understanding what can actually be done with the data.

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

Having research experience is really useful in these cases, especially because people from research and industry have many 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 Tromsø on an environmental DNA project that draws much from my training in genomics.

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

When I started my first position in data science at Nielsen (a TV rating company), almost everyone on the team had a doctorate. in physics, biology or even fisheries. It was a pretty big team compared to where I am now, which has two data scientists and some consultants. The other data scientist I’m working with now has experience in marine data, but not from strictly research experience.

It was not an easy transition for me in the industry at first, but I had really supportive team members who 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 like that I do a lot of different things. This is important to me personally, because I don’t like to do just one thing over and over again. It’s good that sometimes I spend my days talking to people, and other days I focus on coding. I also contribute to the big picture ideas about the direction of our product. So, my favorite thing is that I have my hands on a little bit of everything and I can contact people in other parts of the project because we are such a small team. For example, I like to try to understand what data engineers are doing, learn from them, and contribute to their work.

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

I do a lot of presentations that involve talking about technical things to non-technical people with a really wide range of experience. I also represent various industries or government organizations or universities that are interested in partnering with us. This includes making them understand exactly what we are doing and where we fit in. Therefore, each presentation must be tailored to the listener, so I spend a lot of time setting up presentations and rarely give the same lecture twice.

At first I struggled a little because I’m so used to scientific conversations, where I present all the evidence I’ve gathered, and then I show how I came to a conclusion after turning every stone.

In my current position, small details are not always so relevant. At first, I gave perhaps too much information, as I was worried that I did not have enough data to support my conclusions. Now I learned what was really important and focused my conversations more closely.

What advice do you have for someone thinking about moving to data science?

First, it is important to understand what makes you successful as a person to make sure you pursue a career with opportunities that will make you happy. Second, be patient. It’s really hard to move on to a new career and a new environment. This does not happen overnight, but the skills you acquire during your doctoral studies. will be useful. It is essential that you remain determined and continue to work for this.

Finally, create a support network of people who have the career you want as you go. I always turn to people who have gone through the same path and understand their experience and the obstacles they have overcome. They can impart knowledge that will make it easier for you or even provide resources that you would not have thought of.

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

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