Over the past few years, the data science field has seen massive growth. Time and again, it has been touted as the hottest job of the 21st century. So naturally, a lot of people are drawn towards this field. However, beyond the hype, the reality is that it is still tough to land a job despite the projected great demand for data scientists.
Like any other job interview, a recruiter may not necessarily divulge the reasons for rejecting a particular candidate. A lack of clarity can result in confusion, dejection, and disappointment for the interviewee. This article will explore a few reasons why you may not be getting a call back from the recruiter.
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Lack of Specific Skills
Data science is a field of work that is responsible for solving some of the most complex tasks. This implies that a person working on such a project must possess immense knowledge and skill set. Therefore, an ideal candidate for a data scientist job has skills in each of these three broad areas–Maths/Statistics, Databases/Programming, and Business acumen.
There are plenty of resources available on what skills a data scientist must possess. This overwhelming amount of information often creates confusion. Even people without the know-how of basic analytical skills and numerical aptitude call themselves data scientists. It is essential to understand that an engineering degree is not sufficient to call oneself a data scientist. One must possess knowledge of Statistics, Mathematics, Economics, etc.
It is also advisable to go that extra mile and update oneself on other upcoming technologies, including cloud computing and deep learning.
Technical competency alone is not enough. Companies want people who can help them get more business and make money faster. A prospective candidate must be able to keep the big picture in mind. The ability to communicate even the most complicated technical aspects of your model to other team members (even from a non-technical background) is a highly desirable skill.
Standing Out
Annually, a lot of people enter the data science field. On paper, the know-how of technical and business skills alone is not enough to stand out among scores of applicants. A sure shot way to attract a recruiter’s interest would be to display samples of projects worked on in the past. This is especially useful for freshers.
Employers often look at candidate’s GitHub repositories. Therefore, it is advised to highlight past work on GitHub, display and highlight your best code solutions, and make GitHub a one-stop shop for potential employers to qualify you. To begin, one can look at repositories of other data scientists to get ideas to spruce up your GitHub repository.
Another way to demonstrate your skills would be to participate in different contests, hackathons, and competitions. One can try their hands on Kaggle data science challenges to place relatively higher on the leaderboard. A recruiter may see participation and ranking as something valuable.
CVs and Interviews
Often, in the end, it comes down to how well you ‘sell’ yourself. For starters, a curriculum vitae (CV) gives out a lot about a candidate even before the physical meeting. Therefore, it is important to develop a good CV specifically tailored to the data science job at a particular company.
Few minutes of the interview are enough for the recruiter to decide whether to promote the candidate further or not. It is crucial to prepare rigorously for the same. One must do their homework on the company’s history, the team they are applying for, the organization's projects in the past, etc. Other miscellaneous things like body language, confidence, attire also make or break the deal.
Other Miscellaneous Reasons
We have discussed all the pointers that recruiters look for in a candidate, which could lead to rejection or cancellation of the candidature. That said, a candidate may prepare themself on all the pointers mentioned above and still may not receive a callback. Often the reason might lie within the company policy or hiring procedure. For example, due to applicant tracking system software, a candidate may be stopped at the door based solely on the channel they are coming from. A possible solution would be to come through less frequently used channels.
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