Data scientists are the new assets for any organization, and the candidates are expected to have the right ability and skills to be appointed by many companies.
However, many data science jobs put out by these organizations’ descriptions involves the requirement of a Ph.D. degree to apply. To ensure an effective workflow in data science initiatives, organizations are seeking Ph.D. aspirants, ignoring the challenge of the skill gap in the country.
Data Science, Machine Learning, AI, are notably all new and exciting fields. Some expert believes that organization recruiting for these positions think that these fields are incredibly complicated and that only someone with a Ph.D. could understand them.
Making it invariably a situation that Ph.D. candidates will have numerous advantages over other non-doctorate aspirants in terms of knowledge and available opportunity, although a Ph.D. degree in the data science field will not guarantee results due to the ever-changing technology landscape.
Further, as per research, there are over 4000 jobs for data scientists in the US (a rise of 56 percent from the previous data), which provides ample opportunity for Ph.D. candidates to choose from.
According to Lalithakishore Narayanabhatla, the head data scientist and AI architect at ProVise Consulting, most of the problems that businesses deal with, do not require Ph.D. candidates.
Reasons why data scientists job requirement shouldn’t be only PhD
There is a limited supply of data scientists who possess a Ph.D. Most of them are not even interested in working for a Fortune 500 company and would instead be working specifically for a university or a leading tech company (Apple, Amazon, DeepMind, Google, IBM, Tesla, etc.) These top tech companies are also recruiting undergraduates, though. If they are grabbing all of the senior undergrads, imagine how hard it would be to get a Ph.D.
An individual who possesses a Ph.D. in Data Science/AI/Mathematics/Statistics/Computer Science/etc is one of the most sought after people on the job market. Companies are continually complaining about the lack of talent, and businesses are all interested in the same candidates with skill in data scientists as well. With such high demand, organizations would only go to be competing by offering a salary that is potentially unnecessarily expensive.
3. A Ph.D. candidate might not be what your organization need
A Ph.D. holder is someone that does research, and most companies aren’t doing research. By research, we are referring to creating new models. Most companies have not successfully implemented essential machine learning, and so don’t need to look for someone expensive when a bachelor’s degree can perform the job just fine. It is recommendable that such an organization go after someone interested in applying existing techniques to your data, optimizing them for your data, finding new ways to use them to your business, and getting it operationalized for your business.
4. Online Courses Serve The Purpose
As per the latest trend, the industry is witnessing a massive rise of e-learning platforms and the students enrolling in them, which portrays the potential of filling the talent shortage gaps in the data science and AI marketplace. Analytics India Magazine covers the data science journey of prominent data scientists, who describe their journey and their process of gaining proficiency in data science through best practices, which, majority of the time, includes enrolling in online courses. According to them, one can accomplish great things without a Ph.D. in data science.
5. Your organization might not be a good fit for a PhD
As already established, a Ph.D. has a lot of choices of where to work, all of which are high salaries. If you are not also offering intrinsically exciting or world-changing work, they might get bored quickly. If doing things like “Ad hoc analysis in Excel as needed” is in the job description, there is an expectation that it will be a tedious job.