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The IT skills gap covers a lot of ground. From artificial intelligence to the Internet of Things to cloud infrastructure to cybersecurity, businesses list hiring and retention as top concerns. In its “Assessing the IT Skills Gap” research released in May, CompTIA reported that nearly half of the 600 U.S. respondents surveyed said that the skills gap at their organization has grown in scope and depth over the past two years.
While the skills gap is broad, hiring data scientists brings a special challenge. They need to be equal parts math and stats whiz, programming and computer science super star and subject matter expert. In fact, it’s so challenging that in its glossary of IT, Gartner describes the data scientist role as being broad and deep – requiring collaboration skills to work with business side, analytical skills and data management abilities -- that the “combination of skills that may be fulfilled better as a team.”
What data scientist skill sets should you focus on?
Assuming you can’t bring in a team to fill your data science needs, where do you begin your search. For a detailed step-by-step look at a successful interviewing and hiring process, Wikimedia describes the how it succeeded in its search for a data scientist. It points out that “the most obvious (but sometimes overlooked) issue in hiring a data scientist is figuring out what kind of skillset you’re actually looking for” because different people have different definitions of what the job entails. Because the role is so broad, you’ll be more successful is you can focus on the most attributes for your company.
To help you sharpen that focus, Data Science Central, an online community for big data practitioners, and IT CareerFinder lists several categories and skillsets for data scientists. Here are some of key areas to look for as you begin your search.
Statistics: This applies to developing new statistical theories for big data. “[Data scientists] are expert in statistical modeling, experimental design, sampling, clustering, data reduction, confidence intervals, testing, modeling, predictive modeling and other related techniques,” according to Data Science Central.
Mathematics: These are the types skills you’d expect to find in security, defense and military people working on big data, astronomers, and operations research people doing analytic business optimization as they collect, analyze and extract value out of data.
Data engineering: According to Data Science Central, this includes skills in Hadoop, database/memory/file systems optimization and architecture, APIs, analytics as a service, optimization of data flows.
Machine learning and computer science: As you might expect this includes skills in algorithms, computational complexity and so on.
Business: Data scientists are called to perform tasks traditionally performed by business analysts. ITCareerFinder says that there’s a need for strong oral and written communication skills to present data as a concise story for diverse audiences.” Data scientists also “perform data-mining, modeling and hypothesis generation in support of high-level business goals.”
Software engineering: They bring skills in code development and are fluent in a few programming languages. “Some data scientists have computer programming skills such as SQL, Python, Unix, PHP, R and Java – which they use to modify or develop custom analytical solutions,” according to ITCareerFinder.com.
They are also strong in visualization, spatial data, according to DataScienceCentral.
Pay levels for data scientists
You’re not alone in your search for a data scientist. In this case, misery doesn’t love company. Job site Glassdoor lists more than 26,000 opening for data scientists.
You can expect many of those positions to remain unfilled. McKinsey predicts that, “by 2018, the United States 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 use the analysis of big data to make effective decisions.”
Glassdoors ranks data scientist as Number 1 on its list of Top 50 jobs. So, as you’d expect, these professionals don’t come cheap. According to Glassdoor, the average median salary for a data scientist is $110,000. you can expect to pay $114,000.
Salary.com estimates a data scientist a little higher: $122,023, as of June 28, 2017, “with a range usually between $106,313-$136,763.”
What background equates to successful data scientists?
Given that the data scientist role is a relatively new one -- reportedly coined in 2008 by D.J. Patil, and Jeff Hammerbacher, who led data and analytics efforts at LinkedIn and Facebook, respectively -- knowing what background to look for is challenging. According to IT CareerFinder, successful data scientists combine analytical skills, technical prowess and business savvy to transform “raw intelligence into concise and actionable insights.”
You’ll get different responses to the question, “what does a successful data scientist look like?” However, data scientists who bring domain expertise complete the triangle. That is, when you add subject matter expertise to math and statistical and computer science skills, you arguably have the quintessential data scientist.
The last skillset needed for data science are what are considered the soft skills of time and people management, communications and intellectual curiosity to complement the communication skills to effectively present data as a story for different audiences of varying technical aptitude.
Because there is not a single definition of data scientist, the educational backgrounds also vary. According to ITCareerFinder, the education requirements are the toughest among all IT jobs, adding that about 40 percent of “data scientist positions require an advanced degree, such as a Master's, MBA or PhD.” These schools listed on listed on Master's in Data Science offer advanced degrees and are a good place to start your recruiting efforts.
Companies that are willing to accept undergraduate degrees look for graduates in computer science, math, statistics, economics, engineering and hard sciences such as physics, chemistry, biology, astronomy and geology.
How to network to find hidden data scientist gems
While traditional recruiting tactics are table stakes for companies looking for data scientists, you also need to be creative and use social media tools, especially LinkedIn. However, you also need to ensure you speak data science and think data science. The best way is to join the pack and hang out at communities such as DataScienceCentral, KD Nuggets, Kaggle and StackOverFlow.
If you are struggling to find a data scientist, there may be some good news thanks to artificial intelligence.
According to Gartner, more than 40 percent of data science tasks will be automated by 2020. This will result in increased use of data and analytics by what Gartner calls “citizen data scientists” -- a person who creates or generates models that use diagnostic analytics or predictive and prescriptive analytics, but whose primary job function is outside the field of statistics and analytics.
The research firm also predicts that by 2019 citizen data scientists will surpass data scientists in the amount of advanced analysis produced. Citizen data scientists create more analytics-driven businesses and support data scientists, who can shift their focus onto more complex analysis.