“Consumer data will be the biggest differentiator in the next two or three years.

Whoever unlocks the reams of data and uses it strategically will win.”

—Angela Jean Ahrendts Sr VP Of Retail, Apple

  • A January LinkedIn report declared data scientist as the most promising job in America for 2019.
  • Data scientist topped Glassdoor’s list of Best Jobs in America for the past three years.
  • Since December 2013, job postings for data scientists on Indeed have rocketed 256%.
  • In December 2018 on Indeed, data scientist postings, in relation to all postings, jumped a full 31%, over the same period the year before.
  • The technology job site Dice noted an increase in the number of data science job postings of about 32% year over year.

The WHY behind these spectacular stats is quite simple. “More employers than ever are looking to hire data scientists,” asserts Andrew Flowers, an economist at Indeed. And job postings are coming from a wide variety of industries—certainly not just the tech sector.

Forrester Research analyst Brandon Purcell predicts the demand for data scientists will only grow. “To acquire and retain today’s increasingly empowered customers, companies need to harness the insights in their data to personalize experiences at scale. Data scientists are crucial in turning the massive amount of data companies capture into action. They’ve always been in high demand, but until recently, only large enterprises and digital natives were willing to make a significant investment. Now, almost everyone is.”

Everyone indeed. Although it applies to almost all industries, Kayla Matthews notes the following sectors for whom data science is particularly beneficial:

  • Healthcare
  • Retail
  • Banking
  • Manufacturing
  • Public Transportation
  • Cybersecurity

Matthews concludes, “The industries above — as well as most others — succeed by understanding how to keep customers happy, meet demands, avoid problems and promote efficiency. Predictive analytics are useful for doing all those things and more and could increase the overall competitiveness of individual companies or entire sectors.”

The Impact of the Supply and Demand Gap

Demand in the form of job postings is expected to continue to rise, as more businesses get on board with the impact data science can have on their specific business venture. “Businesses across nearly all industries have realized that analytics can make them smarter and more profitable,” says Jessica Davis, Senior Editor, Enterprise Apps.

The question then is whether the supply of data scientists seeking jobs will be able to meet the demand. As Davis notes, “Career-minded young people are pursuing degrees to prepare themselves for these jobs. That has expanded the talent pool for companies looking to hire, but demand for these workers is still outpacing supply.”

The short-term answer to that question is not yet. As job posting increases are in the 30% ballpark, searches by job seekers are growing in the range of 14-15%, suggesting a noticeable gap between supply and demand.

Feyzi Bagirov, the data science adviser at San Francisco-based B2B data insight vendor Metadata.io, thinks the supply-demand gap won’t close anytime soon. “Universities are having a hard time finding quality faculty to facilitate their programs’ growth, as the salaries in the industry for data scientists are much higher than those in academia,”

So, what’s a data-scientist-seeking business to do?

For starters, expect that the pursuit to find skilled data scientists to be a challenge as the demand for employees with this unique skill set continues to bump against the growing desire for businesses of all sizes and from nearly every industry to be data driven. You can increase your odds for both enticing potential talent and retaining those you hire by considering advice from those well-versed in this dilemma.

  • Revisit the salary factor

While salaries have been relatively steady, some companies have used bonuses and other perks to address salary gaps. But the reality of today’s uber-competitive job market will demand those salary considerations be “on the table;” that management be willing to negotiate and offer competitive compensation packages.

  • Nix the “unicorn data scientist” approach

Purcell says organizations err when they look to combine the skills of data engineer, machine learning expert, and a business executive—the “unicorn data scientist.”

“That’s the wrong approach because those people don’t exist,” notes Purcell. “Look for the machine learning expert who can use R, Python, or SAS and understand which algorithms to apply to different situations. Then, team the person with the other two personas, which you already have in-house.”

  • Execute well-defined job postings

 In this case, less is not more. Be sure every job posting includes a defined set of competencies, a clear description of the problems candidates will need to be able to solve, as well as a profile of the roles with which this position interacts.

  • Recruit from your ranks

For many analytics-based positions, employers prefer both a college degree and several years of job experience. But considering the supply and demand issues in this tight job market, many companies are looking to their existing staff when it comes to filling job openings. Men and women who have proven themselves as dedicated and skilled are excellent candidates to receive additional training. Many in fact will be enthusiastic and eager to accelerate their professional development.

Let the experienced staffing team at RomAnalytics be your partner in all staffing ventures related to market insights, data analytics, data engineering, and any positions that support insights and analytics, including sales, marketing, and client services. Make your talent acquisition and job search smarter by connecting with RomAnalytics today!