The Evolution To Data Scientist

Jan 31, 2017


While I was brainstorming this blog post, a teammate shared another article by a counterpart in the data science industry called The Data Science Delusion. I found it fascinating, but also liberating. It’s nice to know that other people share the same issues and problems that I’ve seen in this space over the past decade. What I took from the aforementioned article is that data scientists tend to have diverse backgrounds, but they have a common desire and curiosity to fix and build things. Some obviously have a M.S. in Data Science, but you may be surprised by how many data scientists have backgrounds in physics, economics, math, or business. Any given data science team is likely a melting pot of various skills and backgrounds.

When I started working in this field, I like to say I fell backward into it—I happened to be in the right place at the right time. I had just finished my graduate degree in economics and I was looking for a challenge. I ended up taking a position at a well-known company as a marketing analyst. I didn’t know what that title meant, and to be honest, neither did the company. The genius of the hiring manager, now one of my closest friends, was that he looked for people that were curious and motivated to build something. Three of us came together, two with MBAs, and one economist (me). What we found was that we were able to leverage each other’s skills to answer any number of questions that we received from the business. At the time, we felt like we were doing analytics—and we were—we each just happened to have the title: “marketing analyst.” This was where I sharpened my skills in the world of predictive modeling, forecasting, writing in SQL and other languages, and all the traits associated with a data scientist.

My next position took me to a large eComm company as a senior statistical analyst. Again, the position was new and the hiring director knew what she wanted. I was one of the two people she hired that had the skills she was looking for. We turned into what I termed “internal consultants,” going from department to department searching for work, building models, and deploying them into the business. It took my evolution in analytics to the next level. E-commerce data is enormous—the structured and unstructured data challenged me to think outside the box. I enjoyed every minute of my time in that position, but I still didn’t have a “data scientist” title. After a few years at this company, I was recruited to another smaller eComm company, but this time to start up the analytics department. This was yet another new challenge for me, but now I was the one tasked with hiring the right people and bringing in the right skills. The talent I looked for had a mix of technical, business, and creative skills. Of the people that I hired or that worked for me, not a single one had a “data scientist” title on their resume. Even my own title reflected advanced analytics vs. data science. We had a successful time and created some cool stuff, but again, after a couple of years, it was time for my next challenge.

That brings us to today. My title is now Director of Data Science, but the fundamental skills and experience I need to do my job hasn’t substantially changed. Data scientist, marketing analyst, statistical analyst, they all tie back to the same essential thing—using the innate curiosity of individuals with unique backgrounds to build cool stuff. The data science group at Elicit is made up of some of the smartest people I’ve ever worked with. We value having a strong mix of young and seasoned professionals so there is not an exact profile that we look for when we are hiring for a new data science position. What we do look for is curiosity, personality, education, experience, and fit with our culture. The result is a world-class team of data scientists that I would trust to take on the hardest of business questions, regardless of the industry.

One thing we can be sure of is that the definition of a “data scientist” will continue to evolve. While I’m not certain what it will mean in the future, I do know that data scientists will continue to be required to build, transform, translate, and deploy solutions to problems that happen every day in business.