Three Common Obstacles When Recruiting Big Data Talent – And How to Avoid Them

May 15th, 2014

Every day, 2.5 quintillion bytes of data are created worldwide. As noted recently by the McKinsey Global Institute, the U.S. alone could face a shortage of 140,000 to 190,000 people with deep analytical skills, as well as 1.5 million managers and analysts with the know-how to effectively apply big data, by 2018. Demand is predicted to exceed supply by between 50 and 60 percent.

The requisite skills to harness big data are difficult to cultivate and found only in a limited cadre of highly sought-after candidates. While the number of qualified individuals is growing, demand is increasing at a faster pace.

When recruiting to meet your big data needs, what obstacles can you avoid in the face of this daunting market picture?

Obstacle #1: Limiting Your Options

Do whatever you can to optimize your choices of qualified big data professionals. Cast a wide net, network extensively and offer attractive compensation packages. Don’t let your search be limited by predefined “silos” that may rule out A-level talent.

  • Don’t look for schooling, look for mindset. Data scientists are multidisciplinary, not experts in a single field, so don’t focus your search solely on one area, such as Ph.D. mathematicians. Hire motivated autodidacts who continually learn through experience and investigation versus those who have completed a specific degree program.
  • Consider hiring right out of college. Look for graduates who demonstrate a strong aptitude to learn. Develop your own homegrown data scientists.
  • Don’t limit your options to Hadoop experts. A data scientist definitely needs technical skills, but don’t confuse this with being an infrastructure engineer. Your ideal candidate should be comfortable interacting with various types of systems including possibly the Hadoop/Map Reduce framework, but this shouldn’t be a filter when trying to find leads.
  • Be prepared to sponsor work visas. A growing percentage of qualified Big Data professionals are foreign nationals.

Obstacle #2: Recruiting Only from “Big Name” Firms

There is not yet a robust big data business community – and there are countless small start-up and early-stage companies ripe with analytic talent. Their names – such as Zoosk, Dataminr, Knewton, AdSafe and Flurry – are not exactly household words. At least not yet.

Databases are expanding with top talent from these companies. If you run a search for data scientists, you’ll learn that a sizable majority work for companies in New York or San Francisco that have between 11 and 50 employees. While you may not have the connections to recruit out of these data companies, your recruiting partner will. Leverage their talent pool and expertise to recruit the best big data professionals away from budding big data companies.

Obstacle #3: “Fake” Data Scientists

A lot of resumes contain data scientist titles, but many of them are actually either programmers or other kinds of analysts. Although it may be just a question of scale, big data shops have issues that someone experienced with SQL or Teradata may never have encountered. These concerns are much messier and more IT-intensive. And you can’t necessarily pick them up as you go.

Be sure to thoroughly vet for skill set. Many professionals call themselves data scientists because they may have run a regression in Excel at some point. But they don’t have extensive technical or quantitative depth. Can they write? Do they have a keen eye for business strategy?

One of the most valuable resources you can have in recruiting is working with a big data recruiting firm that specializes only in this industry. Select Group, Inc., has the experience, expertise and proven track record to help make your search a success. Read our related posts or contact us today to learn more!

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