by Bob Violino

6 ways to deal with the great data scientist shortage

May 22, 201911 mins
CareersData ScienceIT Skills

Rather than wait for want ads to go unfilled, organizations should rethink, retrain, reorganize and reach out to fill the data science talent gap.

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The seemingly insatiable demand for data scientists continues to grow as organizations look for professionals who can glean insights from all the information they are gathering.

In a report released in January 2019, business and employment social media site LinkedIn listed data scientist as the most promising job in 2019, based on data about salaries, number of job openings, and year-over-year growth.

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More than 4,000 data scientist job openings are expected for this year, according to the report, up 56 percent from 2018. Top skills within the category of data science include data mining, data analysis, and machine learning.

The problem is, companies often can’t fill these jobs fast enough because of the shortage of talent. That doesn’t mean they can’t acquire the types of skills data scientists typically possess, however. It might take some creative thinking and persistence, but organizations can deal with the great data scientist shortage in various ways. Here are some suggestions.

1. Look for people who are considering a career change

With all the attention data science is getting these days, chances are that technology professionals — and even non-technology people for that matter — might want to consider moving into the field.

“We look for people from bootcamps that are career changers,” says Pat Ryan, executive vice president of enterprise architecture at technology consulting firm SPR. “These people have a work ethic and confidence in themselves to switch from a career that they know to go into something completely new.”

SPR brings people in as early as possible from careers such as engineering, and coaches and trains them in the needed data science skills. “This is a much longer-term investment” than hiring data scientists outright, Ryan says. “But our experience has been that people with this unique background come with people and situational skills, in addition to the technical skills, that we also have a difficult time finding.”

SPR also looks to hire people with a formal academic background in data analytics that have working experience. “These people have the necessary academic background to understand the mathematics, with the ability to perform some of the necessary development,” Ryan says.

2. Re-train current employees

It can be tough to recruit new technology workers in a tight labor market. As a result, organizations are turning to their own technical employee base to find potential data scientists. Through training programs and the use of centers of excellence, companies can boost the number of internal workers with data science skills.

“We look to train in-house our data engineers that understand how to engineer data solutions, including how to apply machine learning algorithms, with the additional necessary math and statistics understanding,” Ryan says. “This way, they can understand what an R-squared or a confusion matrix is telling them.”

The challenge for many organizations is to close the gap between expertise and experience, says Roger Park, EY Americas advisory and Financial Services Office innovation leader at advisory firm EY. The company trains all its employees in data science, not just those within that field.

“We incentivize our people to actively participate and complete new trainings,” Park says. “For example, we have a program called EY Badges, which allows people to invest in their own careers by earning digital credentials in skills that differentiate them in the market, such as data visualization, AI, data transformation, and information strategy.”

By incentivizing people to earn new badges and offering a strong curriculum, the firm is making training easier and more fun, Park says.

Another tactic EY deploys to help motivate people to learn new skills is gamification. “When new technology comes out and we don’t know where to use it quite yet, we develop reward-based challenges — think hackathons — for our staff to come up with interesting uses and applications,” Park says. “We tap into our people’s desire to play around with new products or tools, to build out new ways to bring these new technologies and skills into our day-to-day work”

3. Leverage mentorships and build centers of excellence

Many organizations already have plenty of data scientists in house, and they can help share knowledge with aspiring data scientists as well as those already working in the field. Through mentoring, experienced professionals can teach newer workers about the inner workings of the business.

“One tactic is to partner new talent with mentors who understand the business,” Park says. “Every good data science department needs three things: people who know how to write algorithms, people who know how to program those algorithms, and those who have business acumen.”

Academia can’t teach people how to marry the business unit with data science, Park says. This is where creating analytics centers of excellence within the organization can help, because they bring together like-minded individuals who continue to challenge each other and use skills to the best of their ability.

Through these centers, “data scientists are embedded throughout an organization and can consult on projects outside their realm,” Park says.

4. Lean on tech

Who says only data scientists can be the ones to leverage information for added business value? Some companies are finding that virtually anyone in the organization can take on some of the roles that data scientists perform — at least to some extent.

One way e-commerce retailer Zulily addresses the shortage of data science talent is by “democratizing” the company’s data to its entire employee base, enabling data scientists to focus on more strategic business challenges and opportunities.

“In our world, where we personalize and launch millions of versions of our sites every day, every aspect of our business boils down to how we approach, analyze, and learn from the data,” says Bindu Thota, vice president of technology.

That’s true whether data is derived from newly acquired customers’ responses to marketing programs or surfacing the most relevant curated sales events personalized for each shopper, Thota says.

The company has created proprietary tools to allow its employees in merchandising, marketing, operations, and other areas to access dashboards and daily reports, which enables autonomous decision making based on whatever activities are going on with the business.

“There are folks within organizations that have unknown abilities and skill sets, and it’s crucial for leadership to help their people practice and develop these talents,” says Jeremy Wortz, a senior architect at West Monroe Partners, a business and technology consulting firm.

“Nearly every organization has data science enthusiasts embedded throughout,” Wortz says. “The key is to have a venue to feature these homegrown skills and assess the latent talent. The term ‘grow your own’ means to take interest in your people to uncover unseen talent — to the point where these ‘unknowns’ become highly skilled scientists and engineers.”

Machine learning automation technologies have made this more feasible, by reducing the complexity of tasks such as building algorithms, Wortz says.

Monroe Partners has uncovered new data scientists internally as well as helped clients do the same. “We helped a bank host corporatewide machine learning challenges — a hackathon — where there were a few junior people working in the bank branches who were able to determine a solution,” Wortz says.

5. Work with higher education

There has been an increase in college and university data science programs, which didn’t even exist until about eight years ago, Wortz says. Organizations should establish working relationships with these institutions whenever feasible.

“While these programs are still being developed, they are showing early, promising signs,” Wortz says. “We know that because we are seeing the skills show up in an influx of junior talent.”

Monroe Partners has established relationships with department chairs and adjunct professors at universities, who discover and refer the firm to promising junior talent.

Companies should form alliances with these institutions and have a robust campus recruiting strategy, adds Brad Fisher, US leader for data and analytics and artificial intelligence at consulting firm KPMG, which has a division of data scientists.

“We keep our recruiting engine running year-long, with weekly touchpoints between the recruiters and the hiring managers,” Fisher says. The firm has partnerships with analytics programs at a number of institutions.

6. Leverage community and diversity

The larger the candidate pool, the better the chances of finding talent. Some companies are exploring how they can reach out into the broader community to get people interested in data science and associated topics.

“It’s important to foster the next generation of data scientists and create opportunities to explore the field at all different levels of education within the community,” Thota says. “We’ve brought in female high school students from diverse economic backgrounds and helped them understand data science, machine learning, and big data in order to build an app in real-time.”

Zulily invites groups of analysts and data scientists to its headquarters in Seattle, and also participates in communities such as the Marketing Analytics and Data Science conference to share its learnings and collaborate with other organizations.

“Similar to our approach to our customer experience, we try to create unique ways to bring interest [in] the technology we build by leveraging data science,” Thota says. In June 2019, the company will host a hackathon with its partner, Major League Soccer team Sounders FC, to use sports data to solve technology challenges.

Although the technology industry has a broad population of data scientists, Zulily does not limit itself to that sector when looking for talent. “From my point of view, one of the biggest mistakes technical leadership can make is rigidity around hiring from the tech sector only,” says Luke Friang, CIO at the company. A strong working knowledge of computer science for engineering is key, as well as

a strong base in mathematics.

“But great talent can come from a number of industries, whether it’s academia, healthcare, or the nonprofit sector,” Friang says. “Those industries often encourage the characteristics we look for at Zulily: inventors, creative problem solvers, people that want to own something and make it better.”

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