There is a huge mismatch between job description and skills capacity in where Data Science graduates are being hired for.
When to hire Data Science graduates?
Companies nowadays are hiring Data Science graduates like crazy, without even bothering to know if that is what their company really needs.
Data Science focuses on using scientific methods and a methodized algorithm to extract patterns of information from structured and unstructured data.
Data Scientists is someone who knows how to extract information and interpret it using statistics, mathematical methods, and with the help of Artificial Intelligence (AI).
Hiring managers should consider the basic definition of being a data scientist before hiring one, so they can have a clear vision of what to expect from the position they are filling in.
Data Scientist does play a big role in companies in analyzing their productivity and efficiency. Some companies expect a lot more from them, which creates a series of confusion and frustration on both parties. There is just a mismatch of expectations from them as to what they are really capable of and bound to do in their field.
Data Scientist vs. Analyst
Data Analysts – Statistician – Data Scientist, these three job positions are interchangeable at some point, depending on the type of company they will be hired. All three are capable of analyzing data in a way that could help the company see its productivity.
Statisticians are more of a long term data analyst. They work more efficiently when presented with complex data that are bound for long term projections. While on the other hand, Data Scientists adapted well to day-to-day analysis output. The process in data science is a bit different when it comes to statistics because it is more of a program based daily output.
But in today’s industry, Data Scientists get paid more compared to Statisticians. Despite the deeper understanding Statisticians have for analysis, they lack the coding skills that companies look for, everything now that works in automation seems to be better and more efficient.
On the other hand, if the company only needs to analyze their productivity through data available, there is no need to hire a data scientist, they can opt for a highly experienced analyst to do the job for them. They can save up on the salary and, at the same time, give the position to a job title suited for it.
If the companies are precise with what kind of output they expect from a particular position, then there will be lesser instances of job mismatches, and lesser unsatisfied workers leading to resignation.
Featured image by Beyond Theory