Companies in a wide range of industries have a pressing need for data scientists.

But these companies are having difficulties understanding what a data scientist does and what value they would bring to their businesses.

As a result, companies are having a hard time finding data scientists. And data scientists are finding it difficult to land satisfying careers in the data science field. You Might Also Like This Blog: How to Land a Cybersecurity Job in 30 Days

Skills required of “true” data scientists

To understand better the causes of these employment challenges, it’s important to have a clear definition of the types of skills and responsibilities a full-fledged true data scientist needs to possess. In essence, a data scientist must be able to lead a data project from its origins to its completion.

There are several steps in this process:

· understanding the data project in the context of the overall business;

· defining what business problem the project wants to solve or questions it should answer;

· preparing the data for analysis;

· creating data models;

· writing software programs;

· enabling the data to be packaged and visualized for easier understanding;

· evaluating, testing, and interpreting the data;

· ensuring accuracy;

· creating a data product;

· measuring and scoring the data product results; and

· deriving meaningful business insights from the data.

True data scientists can perform all these tasks. But they’re tough to find. The steps in this process require a broad spectrum of analytical, functional, and organizational skills.

Many people who call themselves data scientists don’t possess the skills or experience to perform all these tasks. Oftentimes they have about half of these capabilities or less.

Employers struggle to understand their data needs

Compounding these problems, employers struggle defining what their data needs are and how a data scientist can help them solve their problems and answer key business questions. But they still believe they need data scientists to stay competitive.

It doesn’t help remedy this situation that companies use data scientists for a wide range of disparate projects. One company may need a data scientist to run a project with five-to-ten people involved, while another requires 100. And the data project goals are often different.

There is no set way all data scientists must perform regardless of what company he or she works for. “Apples to apples” comparisons in data scientist skill sets rarely exist.

Employees are struggling to understand what data scientist roles suit them

As a consequence of all this complexity, companies remain too ambiguous and uncertain for employees pursuing data scientist careers to make sound, well-informed decisions on the positions.

Too frequently, these professionals struggle to find positions where they can develop and practice the full lifecycle skills required to be a data scientist who can “do it all.”

Too often they end up in narrow roles that only require them to perform smaller subsets of data science responsibilities, but not the entire end-to-end process. They quickly get bored or frustrated because the “data scientist” positions they hoped they signed up for were not data science jobs in the truest sense.

These positions can be career limiting and stifling, sometimes monotonous, and oftentimes not in line with the company’s expectations based on lack of available data, technology misalignment, or too many cooks in the kitchen.

Companies need to figure out why they need data scientists

To help solve these problems, companies need to hold off on opening positions for the sake of not being behind the curve. Before doing so, they should figure out exactly why they need to hire a data scientist.

They need to ask themselves probing questions up front about what data scientists can do and how they add value to their company.

Corporate job descriptions should be precise and candid about what types of data scientist skills they need – and don’t need. Candidates want transparency about this.

To entice candidates to join them, companies should offer them opportunities to do more on-the-job training on all the skills required to lead data science projects from start to finish.

Employees need clarity on exactly what skills they will develop

For their part, employees should ask employers detailed questions about the specific tasks they will be asked to do. Better to know up front that the role does not really require a full spectrum of data scientist capabilities before accepting one – if their goal is to be a true data scientist.