Data scientists have continually been named in-demand professionals year after year. As enterprises build out artificial intelligence and machine learning capabilities, the need for candidates with the expertise grows. Fueling this growth are not only the benefits that AI provides companies but also the competition. One survey found that 80% of enterprises are investing in AI today, and a third of those business leaders think their company must invest more over the next three years in order to keep up with competitors.
Of all of the obstacles to AI adoption, lack of IT infrastructure is continually a concern, as well as finding qualified candidates. Only 28% of companies believe they have the talent to overcome challenges to AI adoption. While those numbers are bleak, the potential of AI has businesses scrambling for talent and technology alternatives to get AI in the enterprise. Roughly half of the leaders (53%) expect revenue increases from AI and the other half (47%) expect cost/efficiency savings from AI investments.
As data science hiring skyrockets, new trends are emerging in this area. Here’s a look at the data science hiring market, changes within, and steps companies are taking to fill the skills gap to remain competitive.
Moving away from the unicorn data scientist
A data science unicorn is someone with extensive knowledge in the areas of mathematics, computer science, and business analytics. An individual who has mastered all three of these subject areas and can use them effectively to accomplish large-scale business goals is indeed rare. Companies that try to hold off to find this nonexistent candidate may find themselves paralyzing their initiatives and stagnating growth.
Now, there is a growing trend of hiring specialists and adding them to a robust data science team. There are many unique roles on the data, IT, and business side of a company that all work together to achieve big data projects. Data science roles have grown over 650% since 2012, and with that growth comes the rise of specialties within the field. Businesses are realizing the myth of the data science unicorn and the value of a candidate with unique qualifications and training.
Data analyst to data scientist
Given the immense skills gap in the data science world, individuals are beginning to upskill themselves to fill these roles. One role that is primed to make this switch is the data analyst. This position helps to improve company decision-making by collecting, processing, and applying algorithms to structured data. Data scientists, on the other hand, typically have a more robust skill set.
With the surge of online courses and self-taught initiatives, however, data analysts can train themselves to become data scientists. Data analysts already have a strong foundation, and while this move may take years of study, it’s not uncommon. Some companies are internally upskilling employees through courses and bootcamps in order to train their own data scientists. The shift from data-based positions to full-fledged data scientists will likely continue as the need for the latter position grows.
The search for security
Aankur Bhatia, Security Data Scientist for IBM notes,
“While there are several products to identify, detect and contain known threats and any indicator of compromise (IOC), there is very little protection against unknown threats, zero-day exploits and newly identified vulnerabilities.”
The search for cybersecurity specialists is certainly nothing new, but the hunt for a “security data scientist” is a relatively new concept. This position has a solid understanding of advanced mathematics and statistics but also have a practical understanding of network security, vulnerability management, and identity and access management.
The cost of cybercrime is truly adding up and expected to reach $6 trillion by 2021. Data science will play a crucial role in curbing these risks. Companies need data that can measure security programs and assess risk. Cybersecurity relies on prevention, detection, response, and recovery. Businesses have invested heavily in detection, response, and recovery, but many still falter when it comes to prevention. Data scientists, or security data scientists, hold the key to prevention, which keeps businesses truly safe.
Hiring a full-scale data science team
Where companies are having a lot of success is hiring a data science team that incorporates members of the business side, the IT side, and data specialists. At least, companies that want to see success with their AI projects should take this route.
Stack Overflow data based on a survey of 64,000 developers found that data scientists are often looking for new jobs. Machine learning specialists topped the list of those who said they are looking for a new job with data scientists following at a close second. One of the reasons for this constant job hunt is the expectations versus the reality of the position. Companies that fail to have a proper infrastructure in place before onboarding data scientists risk losing those candidates. This contributes to the cold start problem of AI and leads to data scientists leaving their positions.
Companies that hire smartly and scale appropriately can reduce that risk. For more information on scaling a data science team, read our blog.