Data scientists, data engineers and business analysts are among the most sought-after positions in America. Yet, many existing and emerging workers don't have the full skillset employers need. While this isn't a new problem, it's a big one. But it can be overcome.
To help you think about what this means for your own hiring, we've worked with the Business-Higher Education Forum to examine the landscape of jobs requiring data science and analytics competencies and skills. The following data-driven predictions and strategies can help you pinpoint your talent needs and target the best candidates for your organization.
Why should you care? The analytics skills are needed now
Data science and analytics skills most wanted in these states
Job postings by state 2015
Data science jobs in innovative industries like information technology can take twice as long to fill than the national benchmark average for B.A.+ jobs of 45 days.1 Requirements for data science and analytics jobs are often multidisciplinary and they all require an ability to link analytics to creating value for the organization. The analytics and technology skills vary widely, but candidates must also demonstrate skills related to problem-solving in the workplace, including soft skills such as communication, creativity and teamwork. This holistic skill set is rare, so you should expect to compete fiercely for T-shaped individuals, as they are now often called, meaning those with a principle competency, plus well-honed broad skills to help them cross functions or domains.
1 Source: Burning Glass Technologies. Burning Glass mined 26.9 million US job postings from 2015 to identify 2.3 million jobs that represent the data science and analytics landscape.
In essence, there are two different markets for data science and analytics jobs. Across the ecosystem, we see two broad families: analytics-enabled jobs and data science jobs.
Common analytics-enabled jobs are Chief Executive Officer, Chief Data Officer, Director of IT, Human Resources Manager, Financial Manager and Marketing Manager. The immediate payoff for raising the analytics IQ in these roles is greater productivity and operational efficiency. These are the people with the know-how to identify customer wants using social analytics, or unusual network activity from real-time dashboards or how to forecast inventory using predictive analytics. It's not surprising that 67% of the job openings are analytics-enabled and require functional or domain expertise outside of data science at the core. What analytics-enabled jobs require is hands-on experience with reporting and visualization software to aid in the collection and examination of data.
Competencies and skills needed for data science jobs are different. They're often the aptitudes that entrepreneurs and innovators most desire. Candidates for these roles have strong credentials (either experience or education) in programming and applied data science. Keeping data scientists and engineers engaged in meaningful work requires an interesting and deep data pool, and a well-organized platform that integrates and makes data available across the company. Competition for these candidates is fierce now, and it is not likely to ease, as more and more companies become digital and change their operating models and talent needs.2
Each of these markets requires its own strategy: sourcing from small pools of experienced data scientists and analysts for one, and employee development for the other.
2 For more on the 10 attributes of digital transformation, see PwC's Digital IQ Survey.
Burning Glass Technologies, a labor market analytics firm, structured the data discovery for the US jobs market. Their task was to build a portrait of demand for skills in data science and analytics. Burning Glass mined over 26.9 million job postings from 2015, the latest full year of available data, to identify:
The data set represents 2.3 million US job postings, where employers are seeking candidates with data science and analytics skills. However, it doesn’t include hidden markets, unposted positions or gig economy jobs. It doesn’t factor for automation of routine tasks. A sixth jobs category, the analytics manager, is not shown. This jobs category represents 1.7% of data science and analytics jobs.
Use this grid to help you think about the skills you need
CEOs tell us they're looking for employees who can solve problems in technology-rich environments and link their work to business value. Yet it's a widely known issue that too few job postings ask for the competencies and skills leaders want. Since there's no common language for using analytics, and different problems require different skills, attracting candidates needs a different approach.
A good team-building plan typically includes:
Together, these three things act as a guide for recruiters to write clearer job descriptions, help management to give clearer development feedback and help leaders to plan the next round of training and hiring as the business grows.
Data scientists and advanced analysts must meet a high bar for educational experience. More than a third of postings for these roles require an M.A or higher.
For analytics-enabled roles, employers typically require three to five years of job experience and a college degree. But the pool of people with the education, interest and experience can be tight relative to demand, so it's good to think of several strategies that could get you the talent you want. Many businesses prefer to look inward for staff who have potential to learn new skills. This is particularly effective for companies that are in the process of modernizing whole job families to use analytics in their roles (your finance, customer experience, marketing or HR functions, for example).
Employees have many options for online and local courses to add skills and raise their analytics IQ, but you'll need to identify which ones you'd consider relevant. You can even accelerate and formalize professional development through partnerships with external education providers.
US employers look for traditional education paths
Where data science and analytics markets are forming
Employers in New York, San Francisco, Washington D.C., Chicago and Los Angeles have the highest demand for candidates with data skills. Businesses in Dallas, Atlanta and Philadelphia are actively hiring for this skillset as well. And data scientists are consistently in demand in Seattle, San Jose and Boston. Companies with good streams of interesting problems and continual flow of data will have the best chance to land qualified candidates. But when the best companies search within a small community of candidates, the market gets distorted and demand pushes salaries up.
We expect more employers to look beyond the hottest markets as part of their overall talent strategy. A plus: large, diverse metro areas are the likely places for performance-based workforce and education initiatives, meaning they will design their programs based on employer needs and measure success on the number of candidates they match to jobs.
Our market snapshot is just that—a quick view of a fast-evolving talent market. Looking ahead, your talent plans will need to factor in automation of routine tasks and how this changes the blend of skills you'll need for both analytics-enabled and data science occupations.
You'll also want to consider how unposted positions or freelance economy jobs change how you compete for the best and brightest. As the end-consumer in the talent pipeline, you have a big role to play in helping candidates navigate this changing market by using job markets and recruiting tactics to your advantage.
Creating in-house analytics courses may not be the fastest and(or) most cost-effective strategy to keep up with the rapid development of the field. Consider other education and training providers that are designed to be more nimble in updating or revising data analytics courses. They often want to help companies that develop stronger views about what's needed in the workplace. In addition to scouring job postings, they are taking note of other forms of signaling for the skills of the future. These signals include the requirements handed to workforce programs, business-higher education partnerships and industry collaboratives that match skilled employees with the right employers.
The 2020 estimate calls for 2.7 million job postings for data science and analytics roles
The landscape has two distinct skills-based markets.