Hiring talented analytics professionals is an uphill battle.
At RBC Canada, Paul Tyndall, a client knowledge and insights professional, says he has found it pays to cast a wide net when recruiting people with analytics talent. “The hallmark to being a smart, successful analytics person today is the ability to translate all that data into business speak,” says Tyndall. “That ability can come from anywhere but my experience has been it’s best to hire people from all different schools and backgrounds.”
That is a useful approach at a large enterprise like RBC. But technology start-ups are in a different position. Handicapped with shallower pockets, a pint-sized referral network and, quite often, untested technology, these fledgling firms must come up with innovative strategies to lure candidates with the machine learning and statistics skills needed to analyze massive streams of data.
Paula Long has first-hand experience courting data scientists to a startup. Long is the founder of EqualLogic, a data storage supplier for large companies, that Dell acquired in 2007 for a whopping $1.4 billion in cash. The deal marked one of the largest-ever cash purchases of a private venture-based technology company, according to VentureSource.
Today, Long is spearheading a new start-up: DataGravity, a Nashua, N.H.-based outfit that wants to help mid-tier companies overhaul the way data is stored so that they can derive actionable insights straight from the source.
“We want to restructure how the data is stored so that, instead of just being a container, companies can look at the content and start to organize it in a way that gives it a vocabulary,” says Long.
Still in stealth mode, DataGravity currently employs 30 people, all but four of which are working on software development. And their numbers are certain to grow as Long continues to bolster the ranks with analytic talent.
Here are 5 lessons any size company can learn from a start-up on how to recruit and retain top data scientists.
1. Hire storytellers.
Given the complexities of data, and the challenges of piecing together dispersed pieces of information, Long says it’s critical that companies “look for people who can look at different pieces of data and stitch them together in a way that makes sense and tells an important story.”
Because credentials like “proficient in Ruby” and “basic understanding of MongoDB” are more likely to appear on a techie’s resume than “great storyteller,” Long says one way to test candidates’ narrative prowess during an interview is “to put out five or six pieces of information – some of them interesting, some of them not, and ask how they’d put the pieces together, what they see in the data, and to tell a story with it.”
Rupen Seoni agrees. Vice president at Environics Analytics, a marketing and analytical services firm in Toronto, Seoni says the most in-demand data scientists today are those that are able “to see the big picture and able to extract a story from the information so that you can actually do something with the data.”
2. Court communicators.
Finding data scientists who can spin stories from yarns of data is one thing. Searching for talent that can both visualize a story and communicate it in business terms can feel a bit like hunting for a unicorn. Nevertheless, DataGravity co-founder John Joseph says finding these people, and bringing them on board, is what can catapult a startup into the big leagues.
“We’re looking for people who can help us communicate [the value of their data] to business users so that when they make a business decision about their customers, suppliers or vendors, it’s a more informed one,” he says.
For the most part, Joseph says DataGravity relies on “a core set of people who have great contacts” to find top data analytic talent.
3. Offer real-world training in entrepreneurship.
The good news for start-ups: they can offer an on-the-job education in how to be an entrepreneur, offering analytics professionals who may have visions of being their own boss in the future the perfect training ground today.
It’s a tactic Long says she relies on to lure top analytics talent. “We like to ask up-and-comers with data analysis skills, ‘Why don’t you come join a startup before you start one? You bring us the data analytic skills and we’ll show you how a startup works and grow.’ That message has been pretty well received.”
4. Offer a piece of equity.
While it’s not uncommon for software engineers to make more than data scientists, Long says, “We’re paying data scientists the same as we’re paying our software engineers. The other thing we’re offering is equity.”
Start-ups like DataGravity also make a point of emphasizing the potential windfall of joining a company while it’s still getting off the ground, or a larger company with handsome stock options. Long’s EqualLogic venture, for example, resulted in a $1.4 billion deal with Dell. All of which, says Joseph, “creates a lot of cachet for us in recruiting top talent.”
5. Guarantee opportunities to tinker.
It’s not uncommon for data scientists at companies with firmly established processes and technologies to “have to live within a contained box. That’s not true here,” says Long. For many data scientists, startups offer something large enterprises can’t: the chance to tinker.
“We tell you kind of what we want to do but we’re looking to you to help us figure it out,” says Long. “There’s a lot more freedom to explore.” A sense of discovery that larger companies would also be wise to offer potential job candidates.
Cindy Waxer, a contributing editor who covers workforce analytics and other topics for Data Informed, is a Toronto-based freelance journalist and a contributor to publications including The Economist and MIT Technology Review. She can be reached at firstname.lastname@example.org or via Twitter @Cwaxer.