Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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How companies can develop internal data science expertise instead of hiring more Ph.D.s

By Nand Kishor |Email | Jul 19, 2017 | 5253 Views

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.

I recently talked with a new biochemistry grad from a major university.

"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.

Second, we already know that demand far outpaces supply when you look to hire data scientists. The median base salary for these treasured folks is now at $110,000/year

Third, tech continues to experience one of the highest employee turnover rates. Part of this reason for turnover could be the lack of professional support. A 2016 industry report2016 industry report from employee engagement solution provider TINYpulse revealed that employees in tech report fewer opportunities for career development than employees from other industries.

All of these elements were evident when I visited with this newly-hired biotech employee who had just earned his undergraduate degree-and why shouldn't they be?

He was making a meager entry-level wage; and he lacked a Ph.D., even though he had been assigned to R&D, so he knew there was no long-term future.

But when I asked him where he would see a growth opportunity, the first words out of his mouth were "high performance computing and analytics... We use a cloud service, but I think my company could do more with that."

Here are some ways that leaders in tech companies can work on giving employees without a Ph.D. a foot in the door:

  1. Continue to partner with universities around the world and to use the HPC facilities at these universities and in the cloud, because the idea of a consortium of universities and companies cost sharing these very expensive resources makes sense. Cost savings that result from this cloud-based resource sharing then give you more flexibility to invest in your own data analysts, and in funding and developing their career paths, as they will be the primary people using these cloud-based assets for your company.
  2. Concurrently take steps to develop internal analytics expertise in the formulation of data algorithms and calculations. To do this, identify incoming employees who might not be qualified for research at the Ph.D. level, but have a strong background in a scientific field like biotech or pharmaceuticals and computing. Then develop a third career path in the company for analytics that can parallel with the Ph.D. hard science path and the business/administration path. A path like this might start out at a junior data analyst level, progressing to a senior data analyst and then on to a full data scientist. The skills battery would include curating data, formulating algorithms and advanced queries, and potentially managing or sharing in the management of processing and storage resources.
  3. If your company can afford it, develop education subsidy programs that help younger employees who are interested in pursuing advanced degrees. Tuition grants can be given in exchange for your employees signing agreements to stay with your company for a certain number of years to "pay back" the grant.

By investing in employees and formalizing new career paths that include analytics, tech companies have an opportunity to reduce employee churn and to build loyalty.

Young employees, too, will appreciate the effort. They will stay with companies longer as they start to see that they have a future, and that they can also start thinking about other life goals, such as starting a family.

Source: Techrepublic