Mistakes Organizations Make When Hiring Data Scientists

The heftiest challenge that data science hirers encounter is absenteeism, job-hopping, and elevated attrition rates. This article throws light on some common mistakes recruiters make when hiring data scientists.

Naming it a data scientist position when it is something else

Most organizations call job roles such as data engineer, data analyst, Machine Learning engineer as data scientists jobs. When in reality they are not. Hiring for the role of a data scientist however allocating assignments that don't sync with the job is a major turn-off. It leads to the candidates quitting their job. Organizations demotivate the candidates in an attempt to waste away their skills. Organizations must be transparent with the responsibilities and the role of the job. The project engagements that the candidate will have to work on should be very clear. This way they set the right expectations.

Organizations are not confident that they intend to utilize data science at all. It is distinguished and many organizations are inclined to build data science positions without being aware of how it can help them. The selected candidates will be clueless about the companies expectations from them. This is a consequence of the company not defining the roles precisely. This confusion leads to motivation and as a consequence the data scientists either get fired or they quit the job themselves.

Not emphasizing enough on problem-solving skills and focusing on statistics and maths

Technical creativity is the fundamental need for candidates applying for the data scientist position. Concentrating on statistics and maths more than is required is a major mistake that organizations make. It is much more than inferring complex algorithms. Recruiters testing the potential candidate's problem-solving skills by giving them real-time tasks should be the organization's approach when hiring.

Failing to analyze the business insight

Many organizations keep the emphasis on the technical aspects and ignore the candidate's consumer engagement abilities. Finding a candidate who can find effective antidotes to business crises and convey them to the patron in a positive way is equally important. Not divulging nominees to consumer impacting decisions and real-time enterprise difficulties often leads to hiring the wrong candidates. The interview should scrutinize the candidate's business understanding.

Placing preference on academics over techniques and practical experience

A standard mistake that all recruiters make is giving more priority to academic reports at the cost of empirical skills. The most important requirement of a data scientist's job is crunching data and unraveling intricate industry problems. A strong educational background is not the only prerequisite for this role. If the candidate's logic and reasoning are not exemplary he will most likely not succeed in his role of developing artificial intelligence outcomes.

Not concentrating on the storytelling abilities of the candidate

Organizations often make the blunder of not concentrating enough on the candidate's soft skills like having a collaborative mindset, effective communication skills, and storytelling capabilities. It is crucial to be a data scientist to be able to share insights with the stakeholders and the clients.

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