How companies can develop internal data science expertise instead of hiring more Ph.D.s Posted on : Jul 24 - 2017

Right now, many data science jobs require a Ph.D. Here's how companies can help employees who lack advanced degrees get on this career track.

 Biotech, semiconductors, pharmaceuticals, and computer science all have one thing in common: they are high tech industries requiring high levels of expertise, and their career ladders in research and development favor job candidates with a Ph.D.

The problem for many young grads, however, is whether they can afford and endure the steep path that lies between a bachelor's degree and a Ph.D.?

Many are already saddled with loans for their undergraduate education, so the financial burden of pushing for a Ph.D. is hard to bear. For others, they see years of deferred earnings while they strive for the "holy grail" of the Ph.D.

"When I graduated, I immediately got a job with a local biotech company in R&D," he said. "But unless you get a Ph.D., there is no future opportunity to advance in R&D, so I have to decide if I want to go into the business side or go back to school."

The career ladders in this company seemed oddly binary and limited. You either get the Ph.D. so you can advance your career, or you go into business.

Instead, I would like to suggest a third career path for these scientific companies:

Giving those with bachelor's or master's degrees opportunities in analytics.

Why do it?

First, industries like pharmaceuticals, medicine, genome research and semiconductors have R&D units that rely on high performance computing (HPC), which uses supercomputers to perform highly complex algorithms and calculations in the formulation of scientific theories, new drugs, genome research, etc. Someone with knowledge of these fields has to develop the queries and algorithms capable of plumbing complex bodies of information so answers can be found, and many new grads could slot quite easily into this work. View More