During an interview for this story, a recruiter laughed at a question that referenced the “three data scientists out there who can’t find work.” With Big Data now ubiquitous in the business world, it’s become difficult to find data professionals who aren’t already employed. Corporate desperation to find data specialists, including data scientists and data analysts, presents an excellent opportunity for the latter. And with so much information available on industries and employers, many data professionals are using their analytics skills to sort through which jobs they want to pursue. But even cutting-edge analytics has its limits, at least in terms of finding new positions. “It’s an interesting idea, trying to apply these skills here, because job-searching is anything but a scientific equation,” said David Ricciardi, president of Proximo, a data intelligence company in Jersey City, NJ. A number of data specialists, employers and recruiters interviewed by Dice Insights agree. Using data to screen for prospective employers is a useful thing to do, but as Jared Franklin, president of Boston-based recruiter Chase Technology Consultants, put it: “Data and analysis is great, but you still need a plan—a plan for interviews, a plan to know what it is you want. The job search today is still kind of old school.”

It’s Not Only About the Data

Data specialists bring a unique characteristic to job hunting. We’ll call it “information discipline.” Not only do they thoroughly research each potential employer, its industry and culture, they do so with a professional skepticism that ensures whatever information they find—whether it’s census data on a metropolitan area or a colleague’s opinion of the company’s culture—is solid. Most people forget that such a hard-driven approach to research goes hand-in-hand with being a data scientist or analyst. For example, rather than simply download data from the Census Bureau’s American Fact Finder and look for trends within a certain city, data scientists will use Google and even research papers to find out what’s driving those trends. “If I was thinking of relocating, I could use that data to learn what a place is like,” said a data scientist in the Midwest, who asked not to be identified because he doesn’t want his employer to think he’s looking for a new position. “I can get a good idea by looking at things like home values and rate of occupancy to identify areas that meet my criteria. I don’t need anything like Hadoop to do it. I can do it with Excel and some Linux shell scripting.” Other data scientists will crunch everything related to company benefits, retention rates, demographics, or other factors. But while such exercises are useful for screening purposes, they have two drawbacks, noted a San Francisco area business systems analyst who works for a global enterprise and also requested anonymity: first, obtaining company information can be extremely difficult, especially for private businesses; second, much of the data that will likely interest you the most, such as retention figures among certain departments, are held close to the proverbial vest. “Once you’ve whittled it down to companies that are a really good fit, it comes down to internal information, who you’re talking to and getting the inside scoop,” said Ricciardi. Yes, even data scientists have to pay attention to fostering their professional networks.

Screening for Potential

How you use data to screen for employers will depend on the sector you’re interested in. One data scientist we interviewed, for example, is interested in startups. “I’d go to CrunchBase and AngelList to see who’s making investments and who’s taking investments,” he said. “Then I’d try to determine how many employees the companies add before they hire a data scientist. That way I can identify my targets.” Still, he noted, finding the right data to run against his analysis would “involve a lot of shoe-leather work.” For all their expertise with data analysis, data specialists stress that planning matters more than anything when it comes to a successful job search. That means goal-setting, research and networking in addition to résumé-writing. “Data people have an approach to research, and it’s not all about data,” noted Ricciardi. “It’s also not intrinsic to data scientists. It’s what all job seekers should do.”