March 28, 2019 Last updated March 25th, 2019 1,520 Reads share

Navigating the Data Scientist Hiring Minefield

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Artificial intelligence (AI) is no longer science fiction. Recently, there’s been a lot of excitement and talk about AI and machine learning among executives. Today, self-training AI programs can automatically learn how to classify new data and produce actionable reports. Furthermore, even though artificial intelligence technology is relatively new, machine learning is already taking the power of AI to a new level.

There’s a massive amount of enterprises that need to leverage these empowering technologies. However, there are not nearly enough data experts to fulfill this need, and as the digital universe rapidly expands, scarce and talented data science experts are feeling the pressure exerted by a grossly out of balance employment market.

Does Your Firm Need a Data Scientist?

A decade and a half ago, today’s data scientists would have never dreamed that their career choice would one day be deemed as sexy. Today, however, they’re the most sought-after business specialists in the world.

Ramping up to launch a big data initiative can be complex and challenging. Enterprise leaders must first understand how much data they have before deciding whether to hire a data scientist.

For executives who want to tap into the latest data analysis innovations, such as machine learning technology, it’s important to understand that these kinds of advanced systems require a massive amount of proprietary information to be of any use. Furthermore, without established key performance indicators (KPIs) it will be very difficult for a data expert to make use of these advanced technologies. These points highlight why it’s vital that business leaders understand exactly what it is that they need a data scientist to accomplish before even thinking about hiring one.

Data scientists can’t perform their magic in a silo. Before hiring a specialist, enterprise leaders will need to leverage the skills of the data engineer to establish a robust data collection program. It’s also essential to have an established support network that will be available for a newly hired data scientist upon their arrival.

If there is no support network in place, there’s no point in hiring a specialist. Although there’s been a recent surge in data scientist training, these newly trained experts need guidance when initially plying their trade in the workplace.

Hiring a data scientist without field experience will only lead to frustration for all stakeholders. Furthermore, any resulting reporting will likely come out skewed.

Analyzing complex data sets is difficult enough with the right talent, let alone for a trained specialist who lacks the necessary experience. This is one area where enterprise leaders would do well to pay a premium for veteran talent.

After hiring a specialist, it’s of vital importance to ensure that they do not have to struggle to gain access to critical information or haggle with department heads to deploy their work in the enterprise. In today’s lean information technology talent environment, the last thing that enterprises need is to lose a highly skilled data scientist after what was an undoubtedly exhaustive candidate search to fill the role. In fact, it’s good practice to secure the services of a reputable data scientist recruiter to assess the important details regarding whether a firm needs this kind of specialist and how to prepare to hire one.

Choosing the Right IT Specialist

Today’s employers are having a notoriously difficult time finding data science specialists. In part, suggest field experts, employers have unintentionally cast this dilemma upon themselves. Unfortunately, there are many job candidates who are willing to exploit the lack of knowledge about data science among employers.

Some unwitting executives have hired so-called data experts who simply use point-and-click tools, such as Tableau and Excel, to perform data analysis and produce visualizations. While these analysts can provide some value to enterprise leaders, they have very little to offer in the form of true discovery and innovation.

The most significant advances in data science that have emerged in the last decade are the product of coders, rather than clickers. Unfortunately, some clickers will demand the same salaries as their highly skilled competitors when given the opportunity.

This is especially important for enterprises that are not technology oriented. Furthermore, it’s important to realize that non-tech firms are competing with technology-oriented firms for the same talent. As a result, human resource experts must always perform their due diligence will vetting data science talent. A thorough discovery up front will save the entire organization a lot of resources and frustration down the road.

Train or Hire: There’s the Rub

In today’s job market, hiring a data scientist is easier said than done. Searching, screening and interviewing job candidates is an intensive process that exhausts time and resources. Conversely, employers take a risk when training in-house information workers who may not have the aptitude to step up to the data science role.

Newly hired data scientists can bring fresh concepts and innovations to an organization. These contributions could be technical, such as knowledge of advanced machine learning techniques like neural networks. Alternatively, a flexible and versatile data scientist that produces fast results can foster an agile organizational culture. Other data science professionals might bring creativity to the table by finding innovative approaches to working with internal and external information.

Depending on enterprise needs, however, executives might want to train in-house IT specialists with whom they’ve already developed rapport and trust. In some instances, the industry experience of internal information technology specialists might warrant training them to work with advanced analyses systems. Employers who have awakened to the potential power of a big data system’s untapped potential cannot avoid this conundrum.

Either way, whether finding or developing talent, executives will need to display persistence and tenacity in their data science candidate search. While advancements such as artificial intelligence and machine learning are impressive, it won’t be long before go-getting executives are hot on the trend of exceedingly powerful emerging deep learning systems that accomplish their work by breaking down problems and processing a large amount of data without a whimper.

 Finance background. Stock Editorial Photography

Ronald Corker

Ronald Corker

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