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Post by Rich Moy on Jul 28, 2016, 12:00:00 PM

Although many organizations have sophisticated processes to track business metrics, it’s difficult for those insights to have an impact if you don’t fully understand what they mean. Enter data scientists, who often have extensive backgrounds in academia and have decided to turn their focus to helping companies understand their data on a deeper level. The value of having an in-house data scientist is becoming evident to many employers. In fact, McKinsey recently predicted that by 2018, there would only be 200,000 available candidates to fill an estimated 490,000 open data science roles. To help you stand out in the extremely crowded marketplace, here are a few things you should know when it’s time to hire a data scientist.

Know Where They Spend Their Free Time

Because the field is always evolving, it’s no surprise to find a number of popular data science forums online. Data Science Central is one of their most relied-upon online resources, offering users both a forum and on-site blogging platform to explore the latest trends. They also turn to sites like KDnuggets, Kaggle, and SmartData Collective for the latest news and discoveries in data science. Additionally, the Cross Validated site on Stack Overflow is a vibrant community of academics eager to discuss the finer points about data science.

While these are great platforms to start conversations with potential data science candidates, keep in mind that the primary goal of users on these sites is to discuss their craft. Feel free to engage with people on these forums, but avoid starting your interactions by telling users that you need to hire a data scientist. If you start by taking a vested interest in what they’re interested in and treat them with respect, you’ll have a much better chance of being able to recruit the people you’re interested in hiring.

Know the Challenges They Tackle Everyday

There’s quite a bit of work that goes into analyzing and interpreting the information they see on a daily basis. Here are a few specific things data scientists tend to worry about every day.

  • Getting multiple teams on the same page. Dan Mallinger, who works with Think Big’s Data Science Practice, told Lifehacker that one of his biggest challenges is getting cross-functional teams on the same page about the type of information they should be after. He adds, “Getting these groups to speak the same language and align priorities is a significant part of the job.
  • Articulating the business value of their work. Michael Li, the founder of The Data Incubator, told the Harvard Business Review that the first quality he looks for when he interviews candidates is the ability to align their work with a business’ goals. “The best data scientists immediately speak in terms of business metrics because they understand that their work has to have value for the organization,” he says.
  • Understanding the engineering behind their solutions. Dr. Steve Hanks told Forbes that a data scientist should know and deliver the infrastructure required to perform any analysis. He adds, “It doesn’t do any good to solve the problem if you don’t have the infrastructure in place to deliver the solution effectively, accurately and at the right time and place.”

Know What Gets Them Excited About Coming to Work

The data science landscape will inevitably change over the next few years, but your knowledge and willingness to stay on top of the latest trends will go a long way when you're hiring data scientists. If you want to stand out from the competition, you should also know what they look for whenever they evaluate a potential new job. We spoke to Patrick Chandler, Chief Data Scientist at Hykso, about what he looks for when he considers a new position.

  • The impact data science has on the products. Chandler tells us that one of the most important things about his work is seeing it have an impact on company products. “I believe most data scientists are always searching for new sets of data to experiment on,” he says, “and create real systems that interpret this data for the greater good.”
  • Strong teams with excellent communication skills. Because communication is such an important part of a data scientist’s job, Chandler tells us he looks for close-knit teams that communicate well. He adds, “Being at ease to point out the flaws in the data, as well as the strengths, makes a data scientist much better at his or her job.”
  • Opportunities to use their data science abilities on all company problems. Chandler says what he enjoys about his work is using his abilities to solve a variety of business issues, ranging from A/B testing emails to better understanding marketing analytics.

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