How companies are creating data scientists in-house

Ten years ago, companies were buzzing about big data, but few organizations beyond giants such as Amazon, Netflix and Google actively recruited IT pros with deep data chops. That has changed in the past few years thanks to improved tools and training, as well as evidence that data can drive business value. Now data science skills are in high demand in nearly every industry.

A recent query of job search site Monster.com showed over a thousand full-time data science postings from employers spanning the gamut from government and healthcare, to consulting firms and software companies. Demand is so great that it is unlikely there are enough long-tenured data scientists or math Ph.D.s to go around.

Plus, identifying — and validating — data science talent is complicated by the emerging nature of the discipline, says Mark Jacobsohn, senior vice president and analytics lead at Booz Allen Hamilton.

“Unlike other professions, we have not seen a single widely recognized certification for data science. There is disagreement on what the term means. That makes recruiting and training more challenging,” Jacobsohn says.

Because of this, many organizations are turning inward to fill their data needs, establishing training programs to gear up current employees with the latest tools and techniques of data science.

Here is a look at how several companies are developing data science talent.

Before establishing a data science talent and development program, it is worth ascertaining where such an effort will pay the most dividends. Because data science blends analytics with business decisions, much can be gained by targeting employees with domain expertise, in addition to technical promise. For many organizations, the best use case for data science to add business value remains marketing and technology platforms with high activity levels.

“Our latest recommender algorithm drove a statistically significant lift in the number of engagements within our application. When expanded to our entire user base, this will translate into millions of dollars of incremental bottom line revenue at Ibotta,” says Bijal Shah, vice president of data products and analytics at Ibotta, a software company that allows consumers to earn cash back on purchases made through an app.

Outside of sales and marketing, data science and analytics add value by improving productivity. For example, GE has worked with steel companies to improve the efficiency of high-value production equipment. In the steel production case, data is obtained from sensors attached to high-value production equipment. Data science analysis can then make predictions on the best time to apply preventive maintenance to help avoid unplanned downtime.

For , which is working to equip hundreds of its staff with data science skills over the next few years, special industry considerations have been a driver for internal development of data science skills at the firm.

“Locating professionals with the appropriate security clearance is a challenge in our business because we work with the government. That is one of the reasons why we are developing our existing staff,” Jacobsohn explains.

For some organizations, data science development is an across-the-organization, cultural affair.

“I am working on developing a culture of data literacy at Qlik,” says Jordan Morrow, data literacy program manager at Qlik, a data analytics platform maker. “Our training and development approach offers beginner, intermediate and advanced training for data science. In our training, the critical turning point is changing the perspective on data. Many people view data as reporting or simple summary statistics. We want to equip our staff to ask deeper questions with data.