Understanding the Data Scientist Gap and How to Mend It

Though data scientist jobs have been ranked on the top of the job lists, for three years running there is still a shortage of talent to fill the huge requirements by the business institutions. The European Commission projects that 100,000 new data-related jobs will be created in the region by 2020, but the fact there are not enough people with the right skills to fill the role is certainly worrying.  Businesses of all sizes have woken up to the fact that data science has the potential to drive efficiencies and IBM projects data science will account for 28% of all digital jobs by 2020. A report from IBM also reveals that on an average, each of the data science requirement remains unfilled for up to 45 days, because those applying just don’t have the required skill set. Over the years, machine learning, big data and data science skill sets are the most challenging domains to recruit for, and if not filled with the right skilled talent, it may potentially create the greatest disruption. It is seen that data science skills are a limited resource within any company which makes this job a very coveted profile making HRs job tough in a highly-competitive market. Data Scientists command a premium and here are the pointers that explain how to mend the growing data scientist recruitment gap-  

1. It is important to Hire them Young

Organisations may find it difficult to hire the best talent with years of experience behind them. Even if they have found them the next question is how to secure this talent in the high attrition job market! It has been seen that many companies hire fresh talent who are right in the first or second year of their job or recent college graduates. Though this type of hiring makes businesses work hard on young talents to make them fit for the job, the returns they get in the form of retention rate is high. If the employees are hired young and trained according to the job requirement, the probability that they would like to work with the top management in the long run is high. Thus, organisations must cash this and get in early, identify talent young and hire them.  

2. The Representation of Women in Data

Women have found a limited presence in the data industry which is increasingly represented by males. Not surprisingly, only around 25% of resources working in data are women. If this representation increases to the same as the number of men, the data industry will see a 50% increase in the total number of people working on projects. Though this may sound fanciful, a magic cannot happen overnight. Businesses should aim to achieve this over the next decade by promoting equal representation at the workplace.  

3. Collaborations and Partnerships to Scout Talent

The urgent need to address the data scientist gap is being taken up at the global stage. Businesses have collaborated with data science educational institutions to fulfill the skills gap across the whole of Europe. For instance, data management and analytics firm SAS collaborated with HSBC and the Data Lab to introduce an MSC course in Data Science for Business. Applicants looking to start their career in data analytics can enroll to this course at the University of Stirling. This course teaches students how to use advanced analytics and apply these skills to real-life scenarios.  

4. The Role of Education Institutes

In the fast-moving technology era, educational institutions have a very important role to play to skill and re-skill resources. They must constantly upgrade themselves with respect to what topics are important today and which technology is no longer in use. Prospective employers seek to recruit resources who were well trained. This training comes from educational institutions who offer industry ready courses, data science boot camps which offer training schedules to make sure graduates have the right up-to-date skills that can help them to a smooth transition into the professional world.  

5. Networking and the Power of Social Media

There are many groups and influencers on social media who discuss about the latest technologies and how businesses can mend the growing data scientist employment gap. Today’s networking doesn’t have to be face-to-face; in fact, great ideas are also being exchanged on social media like Twitter and LinkedIn. In a crux, data science is one of the most lucrative fields promising great returns when it comes to salaries and the added scope to learn. In continuity data science and analytics skills gap would not close immediately but by training graduates and existing staff this gap will be mended slowly. The big question is are organisations ready to put that patience in the real world?