Source: The Wall Street Journal
Sick of hearing about Big Data? Get used to it. Whether you believe analytics is a tired corporate buzzword or the key to future business growth, hundreds of companies are searching, and paying richly, for hires with quantitative skills.
During her three decades at the helm of executive recruiter Burtch Works, Linda Burtch has tracked the rising demand for workers who can understand and manipulate data. She has worked with clients such as Darden Restaurants Inc., Jack in the Box, Leo Burnett Worldwide, Foot Locker Inc. and other big firms to staff the teams responsible for understanding, for example, how marketing affects consumer behavior.
Ms. Burtch says, “it’s a candidate’s market right now.” According to recent surveys of Burtch Works’ contacts, analytics professionals—from entry-level data analysts to executives—earn a median base salary of $90,000 annually, rising to a median of $145,000 for managers. And the group’s just-released study of data scientists, a subset of the larger group, found that nonmanagers earn a median base salary of $120,000. (Data scientists work with large, unstructured sets of data. Analytics professionals generally deal with structured data sets, Ms. Burtch says.)
Not yet a quantitative expert? Better brush up. In a recent interview, the Chicago-based Ms. Burch talked about what companies want, how midcareer professionals can compete and why workers who are left behind could face a “permanent pink slip.” Edited excerpts:
WSJ: I’m a data scientist a few years out of school. Tell me what my life is like.
Ms. Burtch: There’s a high likelihood you’ll live in California and work in the tech industry or in the video-gaming industry. We did a flash survey of our contacts last year and found that 88% [of respondents] had been contacted via LinkedIn by a recruiter; 25% were contacted in the last week. The median base salary for data scientists in the first three years of their careers is $80,000 plus bonus. They are getting multiple offers—I saw a candidate get three offers in a week.
WSJ: How do people at midcareer acquire these skills?
Ms. Burtch: M.B.A.s have looked around and said ‘I’m going to get passed by if I don’t increase my quantitative ability,’ so they’re going back to school, [for analytics programs] like at North Carolina State University and University of Cincinnati. People at the 15-year point in their career are looking around saying, ‘I don’t have digital experience; I’m not working with streams of data.’ These people are starting to get heavily involved in the Kaggle competitions [in which data scientists compete against one another to solve data problems], and they’re taking MOOCs [Massive Open Online Courses]. It’s not easy, and I tell my candidates, you can’t do this casually.
WSJ: Employers are taking MOOCs seriously?
Ms. Burtch: They are. They are not easy classes to get through. You get hands-on work with data that you might not be getting in the workplace. You can use sample projects during interviews. In the data-science world, the employers I’m working with have [data analytics] groups already, so interviewers can tell if a person knows what they’re talking about. They’re very informed about the questions to ask and to know who’s good and who’s not.
WSJ: What’s the biggest mistake companies make when seeking data talent?
Ms. Burtch: Companies really struggle sometimes to understand the talent they need. Candidates have a specific skill set and might be 80% there, and [hiring managers] need to understand whether the candidate has the ability to get to 100%. It’s difficult for nonquantitative people to understand whether someone has the smarts to be able to get there.
I’ve known a lot of these candidates for years and years, so a lot of times, I can advise companies. It’s hard to know from a LinkedIn profile how strong a person’s technical skills really are. Many people on LinkedIn are calling themselves data scientists, but they overstate their skills.
WSJ: How do organizations hire when they don’t understand data well?
Ms. Burtch: One way of addressing it is bringing in consulting organizations that are expert in this area. Beam Inc. wanted to determine how marketing spending impacts sales. They’re using a software package developed by an outside company, and the company needed Beam to hire someone in-house to figure out the software and apply it, and evangelize it across the sales force. The outside software company did the interviewing and screened hires. [The hire started last month.] On the first go-round, Beam hired somebody on their own but it didn’t work out.
WSJ: Where are analytics jobs headed?
Ms. Burtch: In 15 years, if you don’t have a solid quant background, you might have a permanent pink slip. So much of decision-making in corporations is going so quickly toward having a quant foundation. Everything is, ‘What is the pattern in the data? Tell us a story about the math that is going on so we can point business in the right direction.’ Companies want to understand data and how it affects their bottom line.
Analytics is hard. A chief marketing officer, for example, who’s come up the strategy ranks has some idea [about data] but may not know what’s possible. It’s important to get that quantitative foundation really early.
Young people now are going to naturally take all kinds of quantitative programs in college, in grad school, in M.B.A. programs. Fifteen years from now, they will have these skills. Someone who’s 40, they should be concerned. Hopefully their organization will help, recognizing them as a leader and sending them back for training. They need to fill that gap, somehow, some way.