5 Expert Insights About the Future of Data Science Careers

By Stephanie Walden, contributor

In the past decade, data has been compared to gold, oil, and unstoppable natural phenomena; it’s been described as a “deluge,” “torrent,” and “tsunami.”

But within the modern corporate enterprise, the value and power of data may transcend these hyperbolic metaphors. Doug Bordonaro, self-proclaimed technology evangelist and field CTO at analytics software company ThoughtSpot, asserts that data is more akin to an indispensable resource like water than it is to any precious metal.

“We tend to think of new technologies as unlimited gold veins. AI [artificial intelligence] looks like that right now, and data in general looked like that five years ago,” he said. “But I think of data more like water: Everyone should have it, everybody has a right to it. … But we don’t give people open access to untreated sewage.”

Fixing the “plumbing” for enterprise data systems requires careful consideration when building and staffing data teams. As data becomes increasingly central to many business models, the career landscape associated with the industry continues to balloon. In 2018, for example, the role of data scientist ranked at the top of the list of best jobs in the United States for the third year in a row.

Below are five predictions about the current careers landscape for data-related roles in 2019 and beyond—and how these trends may help fix the flow of enterprise-scale data systems.

1. Automation will elevate job functions for data professionals.

Automation presents a bit of a paradox for data professionals: The more they put their own tasks on auto-pilot, the seemingly less assured their job security will be. But even though big data is fueled by 1s and 0s, the human element is still a crucial part of the equation for building successful data ecosystems.

Today, experts predict that automation in the data sector will not upend jobs as some futurists have feared; instead, it will likely enable higher-level job function for data scientists and analysts. Modular, low-code, and even no-code platforms may also soon put the power of data in the hands of non-technical roles, such as sales teams, allowing data professionals to focus on more complex responsibilities like evaluating algorithm effectiveness and potential for bias.

2. Niche, more granular data roles will emerge.

With more than 40 percent of data science tasks predicted to be automated as soon as next year, there’s also potential for increased attention to niche data roles.

Josh Scherbenski, a former lead data scientist for Glassdoor and current chief data science officer at real estate relationship-management startup Riley, posits that more specific roles like dedicated artificial intelligence and machine learning (ML) engineers are starting to crop up on job search sites. “[Businesses] are starting to realize that data science isn’t this all-encompassing field. People are starting to split it into analytics roles, AI/ML engineering roles, etc. It’s becoming more granular,” he noted.

3. There will be a prominent place for data in the C-suite.

Data scientists are increasingly penetrating the highest echelons of organizational structure. Emerging C-suite-level roles like the chief data officer (CDO), chief analytics officer (CPO), and VP of information are on the rise. Although there is still some confusion over where these roles fit—and to whom they should report—the need for ownership of cohesive data strategy is clear.

“From a management perspective, we’ve treated data like another desktop or any other asset that a CIO manages,” said Bordonaro. The problem with this approach, he explains, is that it contributes to a mentality in which data is solely the concern of the data team—instead of a company-wide priority. Assigning ownership of strategy helps organizations consider the bigger picture.

Bordonaro notes, too, that when it comes to semantics, the actual job responsibilities ultimately trump the title. “Personas are a little bit more important than titles. From a senior level, we’re going to continue to see all these fungible titles, but I think what’s really important is … [the responsibility of] owning the culture and strategic use of data in the organization.”

4. Demand for business-minded technologists will increase.

While Scherbenski believes that strictly tech-focused data roles aren’t going anywhere—particularly for ground-level, highly technical product builds—business-minded data professionals are now a hot commodity.

In addition to technical acumen, soft skills like communication are important abilities that managers should seek in their data hires, explains career branding coach Kimberly Robb Baker. This is particularly true for roles that might need to contextualize their work to partners in both business and technical spheres.

“Data is being leveraged as a way to have intimate conversations,” Baker said. “Once the numbers point to where audiences can be found and what they want to discuss, data scientists will need to collaborate with communicators in marketing and advertising to achieve tangible business results. … They’ll [also] need to be able to talk persuasively about complex topics to gain support for their strategies from business leaders, engineers, venture capitalists, and investors.”

It can be tricky, admits Scherbenski, for HR teams to find candidates who have both a wealth of data science experience and people-management skills—particularly since there’s already a supply-demand gap for data talent. But, he predicts, this gap may be narrowing, at least for entry- and mid-level roles. He points at the proliferation of programs like data science master’s degrees that are pumping fresh candidates into the talent pool.

5. Data teams and execs will ramp up cross-team collaboration.

Scherbenski also predicts that the data scientist job description will soon evolve to consist of professionals who can take products from concept to execution. “What’s going to end up being a true data science role is someone who … can talk to a product manager/marketer, figure out the business need, build a product end-to-end, and then hand it off to someone a little more specialized—maybe an AI/ML engineer who can tweak the model and optimize it,” he said.

This focus on cross-team problem-solving extends to executive-level data roles, too. Collaboration across all organizational tiers—ideally spearheaded by a CDO or similar role—can help companies identify and mitigate systemic problems, such as data that’s managed in clunky, bureaucratic ways and is thus difficult to access.

Data teams and those at their helm are also partially responsible for ensuring that data is not only central to operations, but also to company culture. “A CDO needs to change the culture of an organization so that mentally, folks not only know what’s available and have a basic foundation of data literacy … but also—just as importantly—they need to change people’s perspective [of data] from gold to water,” said Bordonaro. “This helps establish a company-wide ethos of, ‘I can’t live without data; it’s just part of how I get through my day.'”