STEM PhDs as Data Scientists

Updated: May 14, 2019

Nanotech NYC is beginning a new series of blog posts focused on exploring alternative career paths for those with backgrounds in STEM, including those with research or industry experience in nanotechnology. This post, kicks off the new blog series. The post was written by Frederick Pearsall with help with Shruti Sharma and Jacob Trevino.

If you are anything like me, you are about to graduate from a STEM PhD program, you have heard a lot of hype about data science, you are extremely interested and excited to learn, and you also have little (or no) idea about what a data scientist does (or what one is). A simple Google search ( leaves much to the imagination and is reminiscent of the first time a professor tried to define nanotechnology to me in class. What got me the most interested in data science is the analytical side, and of course, the programming. That is to say, I understand I may not be able to code like a computer science major, but I can at least think like a PhD and analyze accordingly, using some coding skills in my toolset. In a romantic way, I think of data science as the analysis of all forms of data, not only in the traditional research sense. As an experimentalist, I view data science as a liberating way to learn about the world around us, not so constrained by those pesky natural laws. I also know the available positions for data scientists are growing, as well as their salary (the figure below does not even take into account the position of Senior Data Scientist with an overall average salary of $136,663/yr):

Salaries for data scientists with (top) 0-1 year experience and (bottom) 7-9 years. This is not including Senior Data Scientist positions. Data source:

Now, I could delve into some more statistics and metrics but just what does a data scientist do? Where do they work? The inside of my head started to look like the 3D projection in the image below. I was confused, and I wanted some clarity. We reached out to two incredible PhDs in STEM currently working as data scientists, in hopes of clearing things up. This post is meant to allow you the reader to gain insight on data science as a career option for those with a STEM background. Both interviewees have positions in industry and gave some great advice on how to get some hands-on experience and break into the field.

An actual 3D projection, showing how complicated working with big data can be.

The Interviews

Nikita Butakov received his PhD in Electrical Engineering from UC Santa Barbara in 2018. He is a Data Scientist with Ericsson’s Global AI Accelerator in Silicon Valley. Ericsson is a multinational networking and telecommunications company, responsible for managing over a third of the mobile phone infrastructure market. Their AI accelerator identifies data-science and artificial-intelligence use cases, within Ericsson, and helps accelerate the deployment of production-level solutions.

Bernard Hishamunda received his PhD in Physics from Brandeis University in 2017. He is a Senior Strategy Consultant/Data Scientist at IBM. IBM works with technologies like AI, cloud, blockchain and Internet of Things (IoT) to help their clients transform their industries.

What does a normal day look like for you?

Bernard: It is split evenly. 50% consulting work and 50% writing code (data analytics).

Nikita: A typical day starts off with a brief meeting within our team to discuss our daily goals. The rest of my work day can involve any number of activities, including programming, reading textbooks or papers, interviewing candidates (we’re heavily recruiting right now!) or engineering machine learning models.

What was your career path from your PhD to where you are today, noting any key transition points?

Bernard: I was already familiar with coding and advanced analytics through my academic training. I took big data courses to understand what the field was about. I took online tutorials and worked on data science related projects. I joined business consulting groups for insights into solving business problems while also participating in consulting competitions.

Nikita: Throughout my PhD I worked on side-projects in software engineering and data science, but I reached a key transition point towards the end, when I made the decision to pursue a data science career, rather than continue in academia.

Was this a goal of yours while doing your PhD?

Bernard: My initial goal was to work in big pharma as a liaison between scientists and business leaders, or work for consulting companies (consulting because work changes frequently unlike in academia) on solving business challenges.

Nikita: My PhD goals included publishing papers in high-impact journals, presenting at high-impact conferences, and, perhaps most importantly, figure out what kind of career I want to have post-PhD.

What was the biggest challenge in making the switch to your current role?

Bernard: Relying on other people to get your work done. Lack of academic rigor (when it comes to results) in industry (i.e. deliver quicker/faster solutions but not as thoroughly as required in academia).

Nikita: Breaking into the Bay Area’s highly competitive data science job market was a challenge, that took a lot of work to overcome.

What skill(s) do you rely on most on a daily basis?

Bernard: Data analytics, coding, communication, problem solving.