How to Solve the Talent Shortage in Data Science

Recent years have brought an explosion of data science jobs. Data analysts, data miners, big data scientists, and more similar job titles populate online job boards now. This is a new domain, and very few people, if any, occupying these positions have an academic qualification in this exact field.

Most data scientists come from other jobs or entirely different career paths. Some were engineers; others were programmers, statisticians or mathematicians. Almost any job that requires a solid understanding of logic and statistics can act as a launching point for a career in data science. Here is an outlook on a few trends identified, proposed and analyzed by the likes of PwC, Forbes, and InData Labs.

Call for specialists and fierce competition

As most companies are shifting toward a data-driven approach, there is a higher demand for data scientists, either for in-house development or for software providers offering SaaS products. There is a definite scarcity of these specialists; they need to have a rare combination of skills and a multidisciplinary thinking, which has not been encouraged by most corporate environments.

While some companies are already ahead of the game and have attracted and trained top talent from around the world, newcomers will have a hard time sourcing the right people, and this will come at a very high cost.

The ideal candidate for these jobs is what Tim Brown of IDEO calls a T-shaped individual. This means a person who has vast experience in a niche subject, but curiosity and personal skills. They are gurus in their area of knowledge but are well-read and inquisitive so that they can work with others and create value.

Companies recruiting for data science jobs should be prepared to wait longer to fill these positions than when they are filling more general roles.

The entrepreneurial mindset in a corporate environment

Another reason why candidates for data science jobs are hard to find in a typical corporate environment is that this multidisciplinary approach is more characteristic of entrepreneurs. They need to connect the dots, come up with ideas that have not been tested before, and innovate.

Prior experience in number-dominated fields helps but is not enough. Also, the best results are achieved by those who have an inquisitive approach to problem-solving, instead of just using the beaten track. Attracting or growing such talent will be a challenge for most organizations, as these people are more likely to create their startups instead of spending time on common office jobs.

With data-centered skills in demand, the academic world is catching up and designing data science programs. When these get traction, it presumably will be easier to grow these specialists. But for the next few years, the dominant strategy will be to convert specialists from other related fields.

While it’s almost impossible to find the right candidate on the market, some companies will have to resort to recruiting from within the organization, and help such hires to develop the necessary abilities. The initial education requirements are quite high. All data science jobs require a B.S. in engineering, mathematics or related fields. Some positions may even require skills associated with Ph.D.-level studies. The good news is that these skills can be trained, and knowing the internal workflows and problems helps once a solid computational base is set.

So when preparing your HR budgets for the next year, set aside a consistent amount for continuous education and training of your in-house data scientists. 

Migration from academia

The set of analytical skills required by data science jobs is usually found in people who have already completed academic degrees. Therefore, it is reasonable to expect to see a migration of the workforce from academia to the business world, also driven by higher pay rates.

The only problem with these latecomers, compared to their peers from engineering or other applied fields, is that some of them miss the contact with real-world issues and business context. However, as such hires have the framework in place, they will get used to the nature of data tasks set by their corporate employers.

More job titles

The world of job titles is confusing right now because many organizations tend to use titles as a way of employee gratification. It is not unusual to see employees in mid-level positions called VP just to give them an ego boost instead of a financial one.

In the world of data science, it gets more confusing due to the novelty of the job requirements. It is not uncommon to call any of the following a data scientist: those who perform SQL queries, data cleaners, machine learning roles (from architects to testers) and researchers. As you can imagine, each of these is in fact a separate job. However, until now these roles were not distinctive enough to get a title on their own. 

As data science leaps into the future, there will be less demarcation between data scientists per se and other roles, such as product managers. This already happens in companies, like Quora, that are at the forefront of online innovation when it comes to data applications.

No talk about the future of data science jobs is complete without mentioning the impact on other jobs. As automation's scale and pace increase, data scientists are likely to put others out of their jobs. Although there will be no shortage of work in the future, there will be some imbalance between highly skilled positions and the low-end of the working spectrum.

Emilia Marius is a senior business analyst and project manager software development company Boldare. Combining 8-plus years of expertise in delivering data analytics solutions with 3-plus years in project management, she has been leading both business intelligence and big data projects, as well as helping companies embrace the advantages that data science and machine learning can bring.