Data Scientist jobs rank as one of the top jobs in 2016. There is high demand and low availability making these roles expensive and challenging to fill. As a hiring manager you need to get it right.
What does a Data Scientist do? A data scientist is a hybrid scientists/software engineer/business person and their job is to analyze large amounts of data and turn their findings into competitive advantage for the organizations they work for.
What is the difference between a Data Scientist and a Data Engineer? The Data Engineer is involved in identifying and implementing data analysis tools and working with database teams to ensure that data is prepared for analysis.
What is the role of Data Analytics in a business? According to Ed Burns in his article Bring Data Analysis to the Masses, "The goal of basing decisions on data is simply to make the right decision more often than you would when relying on intuition."
What skills do they have? Their skill set includes analytics, data mining, machine learning and statistics, plus experience with algorithms and coding. They are individuals with innate curiosity and the most valuable are business-minded. Because of this unique combination of skills they are often referred to as 'unicorns'.
What makes them so valuable? They not only analyze data but can turn their research findings into products and services that help companies gain a competitive edge and generate revenue.
How long has there been a demand for this role? Data Scientists have been in demand since around 2014. This is about the time large organizations began to pay attention to 'big data' and see the potential it had to help them stay ahead of their competitors and assist their transformation initiatives. Many had made investments in data warehouse initiatives.
What is the median salary for a Data Scientist? According to job listing provider, Glassdoor Inc. the median salary is $116,840 U.S.
Craig Stedman, Executive Editor of SearchBusinessAnalytics recently wrote an excellent article titled "Building Data Scientists Teams takes Skills Mix and Business Focus" which appeared in Information Management Digest. Some of my take aways from this article, which was based upon an expert panel of managers of data science initiatives, from the recent Strata + Hadoop World 2016 Conference included:
Data Scientists are hard to find. The role requires a unique combination of technical skills coupled with business acumen and analytics.
Hire the right people at the right time. If you hire too early your data scientists could get bored with nothing to analyze.
Look for people that have a 'get things done' attitude.
Identify candidates that are grounded and can understand what's feasible, what's doable and what's important.
Communication/educator skills are important in these jobs - so that analytical findings can be explained to business executives in understandable terms.
To ensure co-operation between data scientists and data engineers have them work in the same team.
Allocate time for data scientists to do exploratory analytics work to keep them engaged.
Emphasize the business problem they are solving and the impact of the work they are performing on the organization.
How long can it take to fill a Data Scientist role? On average companies are saying it takes 6 months or more to find the right candidate.
Where can I find Data Scientist candidates? There is a war on for technical talent. With huge numbers of baby boomers retiring over the next few years it is likely to intensify. The first thing you should do is develop an overall technical talent master plan. By taking a holistic approach to the problem you will feel in control and understand your options.
Some of the tactics you can employ to specifically find Data Scientist talent includes: Working with a recruiting company that specializes in hard to find talent is one strategy.
Another longer term tactic is to partner with a local university or community college that is offering programs producing Data Scientists. Expanding your Early Talent or Internship Program to include data scientist positions is also another way to identify the talented 'unicorns' that will be a fit with your organization.
Other organizations with high demand for Data Scientists, have developed unique Boot Camp programs to develop their own pipeline of candidates. There are also some interesting intensive Data Scientist Boot Camps which are popping up.
What if I can't find or afford a Data Scientist? There is a current move towards putting a team together that collectively would have the skills that are required. The cross-functional group could include a business analyst and data quality engineer, as well as product managers that tie the analytical effort back to business objectives. A shared interest common to all is recommended. R is a popular statistical programming language that could provide the common thread.
For more tips and techniques for finding Data Scientists read Searching for Unicorns - The Data Scientist Skills Gap 101. For information on finding, developing and retaining hard to find digital skills contact us!
Susan Dineen is the CEO of LegacyNEXT Strategies (www.legacynextstrategies.com). Multifaceted talent roadmaps to address the Tech Skills gap from Mainframe to Digital Skills Gap. You can contact her at email@example.com