As a former data scientist (now product manager) at Square Root, I’ve come to learn there are two different types of data scientists out there. When looking to hire or contract with a data scientist or consultant, it’s important to understand the types of skill sets that “data scientists” typically have. In talking with colleagues across the industry, I’ve found that they typically fall into two categories:
- The Algorithm Scientist
- The Business Scientist
The first group of data scientists are hired to design, build, and optimize “learning” systems. They have a penchant for deep, rigorous thinking and a taste for solving difficult problems. I call this group “algorithm scientists”: They thrive under the challenge of understanding the minutiae of the systems that they build, and often have a master’s understanding of math and computer science.
A good (and brilliant) friend of mine from grad school falls into this category. As the lead data scientist for a streaming music company, he built and refined their music recommendation engine. The algorithm scientist should be challenged with hard problems with longer horizons—for example, improving Amazon’s recommendations by 0.1% would potentially drive millions in new revenue.
The second group of data scientists go broad—they tend to be generalists, working across an organization to solve business problems. I call this group as business scientists because they apply scientific reasoning to test and optimize a company’s business model and operations. While the algorithm scientist sits at the confluence of Math and Computer Science, the business scientist has a better understanding of how to dissect business problems.
The person who excels in this job is fundamentally different in skill-set and character than the algorithm scientist: This person is more of a “hacker”, doesn’t form deep connections to a problem space, and likely has a strong entrepreneurial streak. This person should be challenged to work across silos, connecting disparate parts of the business.
I definitely consider myself a business scientist. In graduate school, I spent many years building beautiful, but ultimately untestable mathematical models of the universe. When I took my first job as a data scientist, I realized the business faced difficult decisions every day, and those decisions were often made according to the manager’s instinct. Adapting to my new career, I immediately learned how much I loved changing that person’s opinion with numbers.
That love was something I brought to Square Root when I joined three years ago. Here we hire naturally curious business scientist-types who are superb communicators. While members of our team may have deep domain expertise in a single class of business problems, all of our team is comfortable working with our clients, on any project. This creates a powerful network effect in the team: When one of our naturally curious business scientists attack a problem, they benefit from the unique perspective of everyone on the team. The end result is a superior solution for our clients.
Ultimately, your hiring plans should include an honest assessment of the types of problems you experience in your business. This will make your engagement with a data scientist more productive for both of you.
If you want to join me in solving complex problems for some of the biggest names in auto and retail, check out our open positions now!