The Data Scientist Shortage is Huge. Here’s How to Beat It.

Organizations of all sizes have realized the potential of data science to drive productivities, mine new insights from years of collected data sets, and else transform their businesses. Today, from Zillow’s home price forecasts to Amazon’s recommendation engines, usages of data science have become more and more prevalent.

But while data scientist has been rated the “#1 Job in America” for three consecutive years, according to Glassdoor, there’s still a shortage of talent to fill the massive requirement employers have. Faced with this dire scarcity of talent, business owners who want to make the most of data science can’t depend on half measures and casual recruitment processes. What they really need is- a planned roadmap toward building data science skills and an effective hiring and resourcing plan.

In emerging with a roadmap to use data science effectually at your organization, you need first to analyze your precise requirements. The fact is that various businesses, particularly small- and medium-sized businesses, don’t really require innovative data science systems to get a leg up on the competition. Still, there are some skills that every data science operation at every organization needs. It includes Modeling, Data Munging, Communication/Storytelling, etc.

Are there any areas where your data scienceteam is particularly weak? Recognizing where your resources are deficient and where you’re already strong is the key to developing your roadmap to taking benefit of everything data science has to offer.

Once you’ve devised your skill-building strategy, your next step is to recruit for and resource the roles that will support it. As the data science field is relatively new as compared to other areas in the business world, you may pose some problems sorting out all of the different titles created in current years to recognize what different data science roles do. Don’t pay too much attention to titles; in some organizations, one person fulfills various roles.

Hiring for the job roles that match up with the skill areas you’re presently lacking will assist you to head down your strategic roadmap. Just keep in mind that in the hiring process, discovering the right person does not come down to who can solve every challenging data problem you throw at them in an interview. It’s much more valuable to be extremelychoosy about figuring out which difficulties can impact your business most, then hiring appropriate candidates to support solving them.

One major mistake that organizations make when making a data science team is overlooking their strategic roadmap in favor of following a “unicorn” candidate. Popular terms under the categories Deep Learning/AI (comprisingKeras/TensorFlow), Big Data (like Spark/Hadoop), and Machine Learning (such as Reinforcement Learning, Natural Language Processing) are in fashion as they’re comparatively new techniques and platforms. And it’s not unusual for executives to hear the buzz,and then decide that they must seek candidates focused on the latest-and-greatest too. But job descriptions that comprise all or most of the terms above rule out candidates you really need to solve your particular business problems.

The key is to be laser-focused on your organizational issues at hand. Are you looking to improve your inventory and distribution projecting ability? If so, a person with a solid understanding of statistics and experience in building regression models employing time-series data may be all you need. Keep in mind that you don't need someone with a Ph.D. in Computer Science or Stats with 10+ years’ experience to do this; there are various entry-to-mid-level candidates from varied STEM backgrounds who have strong practical knowledge in these areas.

If you’re still uncertain where to begin and how to identify your data issue, try and find someone who effectively led a data team that has done something similar to what you’re trying to achieve. They can help you understand your issues, how it translates into the data you need to gather and analyze, and what kind of various data science skills you’ll need to improve in on when searching for candidates. This sort of early-stage advice can aid you to avoid many of the pitfalls that companies face when looking to hire talent for their new data science team.

If you also wish to make your career in the field of data science, then masters in data science from a reputed institution can help you acquire essential skills to build a successful career as a data scientist. Top institutions have top-class faculty and resources to train you in the field of data science.