What’s next for the data science and analytics job market?

The best jobs right now in America include titles like data scientist, data engineer, and business analyst. Yet too often, employers find it tough to make a match. Business wants different skills than those possessed by existing and emerging workers. And while this isn't a new problem, it is a big one.

Why should you care? The analytics skills are needed now

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.}

Which data skill sets are needed most in an array of industries

Use this grid to help you think about the skills you need

CEOs tell us they're looking for employees who can problem-solve 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:

• Profiles for data science jobs and the roles they interact with
• A well-defined competency map for these jobs identifying the the principal competency for each, including the knowledge and skills required to do the job well
• A clear description of the problems qualified candidates will be asked to solve, so that recruiters can prioritize what to look for

Together, these three things act as a guide for recruiters to write clearer job descriptions, management to give clearer development feedback, and leaders to plan the next round of training and hiring as the business grows.

Where data science and analytics markets are forming

Five big metro areas hire for data skills the most: New York, San Francisco, Washington D.C., Chicago, and Los Angeles. There's also active hiring in Dallas, Atlanta, and Philadelphia, plus the places known for hiring data scientists: 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.

• Signal for competencies, not just skills, to build your data science team. A competency framework for your company's data science and analytics jobs is a clear signal to policymakers and education providers about what you need. While skills in Python or R may be preferred now, for example, a competent coder can and should demonstrate the ability to learn new coding languages and programs. It may be more difficult to teach other skills on the job, like critical thinking and teamwork. When you understand competencies—what a person must be able to know and do—you can use this framework to signal to emerging and existing talent what's expected of them, even as technology changes and automation of routine tasks becomes more common.
• Identify your high-potential employees for data analytics work and invest in them. Work-based learning opportunities, where teams can work on real business problems, help people gain much-needed experience. Apprentice-mentor style arrangements or specialized development paths can help you pay attention to existing employees with potential for a future in data science. If they already know your industry and business, upgrading the skills of statisticians or data-savvy programmers—anyone who works with algorithms—could have a significant return for your investment.

• Connect with other catalysts in your immediate communities. Employers can flex some muscle and influence policymakers and education providers to do more. Act as a community and join a collaborative or other organization focused on creating a competitive workforce for the 21st century. The Business-Higher Education Forum, and the Business Roundtable have active efforts to close the data science and analytics skills gap for American industry. Local workforce development programs like NYC Tech Talent Pipeline can help you understand how to signal for the right skills and match you with candidates within your community.