Lindsey Thorne, Manager of the Open Source & Big Data Practice at Greythorn Lindsey has been in HR and recruiting for more than 12 years, and after narrowing her focus to the open source and data science market in 2012, she’s built a reputation for being the one recruiter “inside” the industry.
Mary Kypreos, Recruiting Manager of the Open Source & Big Data Practice at Greythorn Mary is lucky enough to combine her passion for hiring the best talent with her love of big data, and one of her specialties is finding data scientists (and actually knowing how to).
“What makes me stand out as a candidate for a data scientist role?” We’ve heard this question asked many different ways – over beers at a Meetup or in a Reddit forum. Whether you’re a new data scientist or just researching the career path to see if it’s for you, it’s important to understand the basics of growing your career. We spoke to Lindsey Thorne and Mary Kypreos who answer some common questions about what companies are looking for in a data scientist, how to stand out as a candidate, and the best ways to start networking.
This is a tricky question, because it completely depends on what type of data scientist the company is looking for—there is no standard definition (though the book Analyzing the Analyzers has four loose categories that are helpful).
In general, most of our clients look for an individual who graduated with a technical or quantitative PhD, which could be anything from mathematics or statistics to physics or computational linguistics. In addition, they are usually looking for someone with at least one industry position outside their PhD—without that experience, you’re often considered a junior candidate due to a potentially longer ramp-up time in the role.
As a candidate, you’ll also want to emphasize any hands-on engineering experience you possess, since data science teams and engineering teams continue to work closely to achieve a company’s goals.
This depends on what kind of data scientist the company needs: statistics focused? Machine learning focused? Hands-on business case experienced?
The experience needed for each of these would be different. For example, not every company is looking for a data scientist with a PhD—that requirement is often removed if they want someone with 5-10 years of relevant experience working in the same space. Other companies, however, do need a candidate who can work through ambiguous problems using a scientific method-like process, which is often learned through doctoral work.