Lindsey Thorne_Low Res
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
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.

When a recruiter or hiring manager is looking for a data scientist, what indicates that they may be a good candidate?

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.

What kind of experience are employers looking for in data scientists?

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. Just as many companies need someone who is equally strong in analytics and engineering—and those skills can be gained from a variety of backgrounds and degrees, such as starting out as a statistician who takes on an engineering role, or someone with a bachelor’s degree in a science-related field who earns an advanced degree in computer science, etc.

So before tailoring your resume or cover letter, you need to ask, “What does this company need their data scientist to do?”

How can a candidate early in their career stand out?

Get the hands-on engineering experience first; this way, on average, most companies will know that you come to the table with more than just “big picture” concepts. They should know that you also understand the fundamental challenges their teams face. We’ve seen more and more companies looking for data scientists with both engineering and analytical skills, and this will set you ahead of the curve if the industry continues to move in that direction.

If you do nothing else, find a way to gain practical experience that is relevant to the industry or position you’re interested in. Whether you are graduating with your PhD soon, taking courses to supplement your education, or looking to transition your career, practical experience will always give you an edge over a candidate without it.

In which communities should data scientists network and be active?

There is no one community out there that is better than another, but there surely is a community that would interest you more than another. Be sure to spend some time researching ones that you find interesting and where you think you could contribute.

Check out and other professional networking groups in your area, and regularly attend and become well known in your group! Follow trends, people, and companies you think are doing important things on Twitter, Facebook, Stack Overflow, etc., and—more importantly—contribute to the conversation. You’d be surprised how many people make friends and professionally network over the internet without ever meeting in person!
We also recommend you attend and even submit talks to any conferences or training sessions in your area, especially if they could give you practical experience (sometimes these events have scholarships or subsidies available). Once you’ve found interesting groups and communities, it’s important that you become an active and visible addition to the community, no matter the medium.

At the end of the day, it’s important to remember that every company’s requirements for a data scientist will be different—and you will not always be the right match for every opportunity. That is not a reflection on your skills or experience, but on the wide range of areas in which people labeled “data scientists” work. Ultimately, you need to understand your strengths and what you have to offer an employer, and communicate those well. If you aren’t sure how to do that, working with (ahem) a recruiter that specializes in data science roles might be your best bet.

Like this article? Subscribe to our weekly newsletter to never miss out!

Previous post

"From my point of view, FinTech won't exist as a stand alone industry..." - Interview with SolarisBanks' Peter Grosskopff

Next post

The Human Element of the Data-Driven Campaign