Diversity in the workplace is an impractical ideal without a talent pool of diverse candidates from which to choose. And qualified minority candidates, especially women, have always been a rare breed on the tech hiring scene. But is that about to change?

“We’re definitely seeing more and more women getting excited with big data and analytics,” said Ziya Ma, vice president of the Software and Services Group at Intel Corp. and a founding member of the Women in Big Data forum.

After making a commitment of $300 million to close the diversity gap, Intel aims to achieve full representation of women and underrepresented minorities in its workforce by the end of 2018. Ma spoke with Lisa Martin (@LuccaZara), host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the Global Women in Data Science Conference in Stanford, California. They discussed the push to close the gender gap within the high-tech industry, as well as the skill sets required for a successful career in data science. (* Disclosure below.)

Know what job you want, then take it slow

As a career coach for female professionals, Ma is often asked for advice on the most important skill sets for data science candidates. With a huge range of opportunities within the field of data science, the first priority is to decide which is right for the individual.

“You have to ask yourself, what kind of job you want to move to and then assess your skill set gap, and work to close that gap,” Ma said.

Taking it slow is another piece of advice offered by Ma. “Data science appears to have a high bar today, and it may be too significant a jump from where you are to a data science field. You may want to move to an adjacent field first,” she said.

Collaboration and openness are the soft skills Ma considers critical. Data science is no longer a loner’s game, and being comfortable working closely with industry experts, academics and the open-source community is important. “Let’s say you are applying data science solutions to solve a real business problem. You need to work with a domain expert … who knows the business operations and processes in that field really, really well,” she stated.

Openness helps in understanding the true needs customer, Ma pointed out, describing how a few years ago there was an assumption that Intel’s customers wanted high performance. In fact, for most clients the larger goal was to apply deep learning to existing application infrastructure. “Once we were open-minded to that learning, that totally changed the conversation,” Ma said.

Although inclusion of women in the tech workplace is advancing fast, it is still important to reach out to girls and women in high school and university to ignite their passion for data science. “We have to get more and more girls getting into the science and tech sector, and we have to start from young,” Ma said. “I think we really need to build our pipeline and a pipeline for our pipeline.”

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the Global Women in Data Science Conference. (* Disclosure: TheCUBE is a paid media partner for the Women in Data Science Conference. Neither Stanford University, the event sponsor, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE