Does Becoming a Data Scientist Require a Ph.D.?

It's more likely that businesses are accustomed to hiring from a fairly small pool of academic candidates

Data scientists are in high demand, and recruiters are having difficulty filling open positions with suitable candidates. This raises the issue of what qualifies a candidate.

Only a third of STEM degrees are earned by women, despite the fact that they make up 55% of university graduates worldwide. Women only hold 18% of industry employment in the US, despite a boom in the data science market. The proportion of Black and Hispanic STEM degree holders, however, is only 7% and 12%, respectively.

There is no shortage of candidates with valuable data science skills in the employment market. It's more likely that businesses are accustomed to hiring from a fairly small pool of academic candidates.

Increasing Hiring Standards to Match the Situation

Degrees are no longer the only criteria for hiring. Instead, it pertains to how employers must assess employees' skills and value beyond their technical knowledge. A varied team of coworkers is essential, particularly in 2022 when data scientists will value cooperation, creativity, and business acumen just as much as coding.

There is a rising need in organizations today for AI that goes beyond the theoretical, AI that can be used in a practical setting and promote corporate success. Businesses must therefore make it easier for candidates who did not attend STEM-related higher education. In order to deploy artificial intelligence in many industries, there is a growing demand for data scientists with commercial expertise or for people who excel at cross-organizational collaboration.

It goes without saying that understanding a few programming languages is a prerequisite for every position in data science. However, the candidate pool will be smaller and more homogeneous the more specific the requirements. According to my experience, promoting the various sizes and shapes of a contemporary data scientist will open up the profession to a wider variety of individuals.

Purchase of a Diverse Pipeline

Additionally, employers should widen the entry-level course requirements beyond STEM degrees. Building models that serve specific needs will become more important as commercial investment in AI rises and the data science industry develops. Instead of taking a bottom-up strategy, data science teams will need to prioritize getting an application online and providing value as soon as possible, before concentrating on iterating and improving it.

Hiring from related professions, such as psychology, might lead to the identification of exceptionally good candidates, given the wide range of abilities that create a great data scientist. Employers will uncover individuals who can react to the increasingly commercial abilities required in these areas if they broaden their attention beyond technical talents throughout the recruitment process.

Changing Your Strategy to Stay Responsive and Relevant

As businesses set diversity goals for their workforces, creating these routes for diverse talent is a crucial next step. Expanding the hiring pool only makes room for fresh, edgier perspectives that guarantee all data science models developed are impartial and inclusive of the entire community. Candidates from diverse origins and perspectives also provide a range of experiences and information that will spur creativity. Companies who change their talent strategies and take into account the shifting workforce will remain competitive.