Hiring the wrong type of Data Scientist can take you down the wrong avenue

Whilst the sector can be complicated and bewildering, one of the main problems facing businesses is the fact that they are often hiring the wrong data scientist for their needs. As crazy as it may seem, there is deep confusion within firms that require data professionals but what can be done?

The blame does not just rest on the shoulders of those who are hiring. Prospective candidates may be averse to highlighting the mistakes made by employers if it means that they do not get the job.

Some who might be referred to as data scientists, are closer to statisticians and data analysts. They may work with Excel and they may create business insight visualisations. They may be nicknamed ‘clickers’ and they have a positive role to play in businesses, but they are not coders and cannot make or replicate the breakthroughs that companies desperately need during these times but may be earning the salary of the more qualified coding data scientist due to the confusion.

The Senior Economist for Glassdoor, Daniel Zhao, states, “This muddling of job titles is changing the composition of the data scientist workforce and holding down wages.”

This is having an effect in two ways. ‘Clicker’ data scientists may be receiving higher salaries meant for ‘coder’ data scientists, or they may be receiving the normal salary for someone in that role. This gives the employers the false impression that data scientists fit into this wage bracket and any data scientists with higher expectations are being unrealistic and thus, will never be hired.

This all starts at the job spec stage. A lot of these descriptions do not really specify what the role requires. This can turn away seasoned data professionals and leave the door open for those who are less skilled. Companies accepting candidates who have 50% of the requirements can be hiring the wrong data scientist. You must not be afraid to hire someone who has 100% of the requirements due to suspicion or fear of over-qualification. There are many facets of this problem because businesses can, conversely, scare away good prospects by demanding too many years’ experience in a certain field or too many qualifications that are not relevant. Perhaps the company has decided to cover all bases by advertising the role as a senior role but research has shown that this scares away perfectly-suited potential candidates. Specificity is the watchword here, as it will guarantee that only the right scientists are interviewed and hired, and it also means that collectively, data as a whole will move forward into a bright future.

Data wrangling and cleaning big datasets require programming and coding skills. This is the meat and potatoes of the data scientist and it cannot be done by someone lacking coding skills. Point-and-click has its uses but that software does not have the complexity to do the heavy work. Data scientists need to be able to code in languages like Python and R.

This kind of mix-up was bound to happen when you have companies launching data projects for the first time or seeking to exploit their data without a full understanding of how these goals will be achieved. Each discipline uses the same or similar terms so it is understandable that management who are just learning the terms would assume that these roles are the same. This naivety can be countered by employing or partnering with experts who can recognise higher skills, who know how to build data teams and what the structure of those teams should be. This isn’t just a problem that can we waved away as ‘geek stuff’. Your company’s bad data analysis could lead to lawsuits and heavy fines, so it is worth bearing in mind that nipping this problem in the bud before it gets out of hand will pay off well in the future.

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