The data science domain is witnessing skyrocketing popularity in the industry. According to one of our studies, there are 97,000 analytics and data science job positions available, and out of that, 97% job openings in India. As this number continues to increase, companies across the world are taking different approaches to hiring the best talents.
However, amid all the hype and talks about this “sexiest job of the 21st century,” a huge problem has emerged — misleading job descriptions. The data science industry has become a victim of this issue, and a lot of aspirants, as well as professionals, are being affected by it.
Further, the data science domain is still new to many companies and even they lack the knowledge of where and how to implement. For example, there are companies that fail to figure out whether they need a data analyst to process and perform statistical analysis or they need someone who can provide them with a way to make the best out of the data they have. Since this is a relatively new domain, these different roles and titles also create a certain level of confusion. What happens is that rather than putting out a specific job description, companies end up using data science as the main term.
Rise Of Irrelevant Job Descriptions
Most of the data science job descriptions with multiple roles mentioned divide by forward slashes are confusing. They seem like a copy-pasted description of different data science roles. Companies that know how to onboard the talent, always stick to descriptions which don’t rely on corporate jargon.
Some of the major problems with these descriptions or even the company could be:
- The person who’s posting the job is not aware of what the department is looking for
- The company is not transparent in its communication
- The company just heard about data science and how it’s transforming businesses and just like a trend-follower, the company just hopped into the domain
- The company doesn’t have an in-house data science team, architecture and experience
- The company is a complete rookie when it comes to hiring
- Or maybe they just want someone to be on the domain and showcase they have a data science department as well
How It Affects Candidates
A data scientist invests a significant amount of time and money in mastering his/her field. It also takes a tremendous amount of dedication to solving some of the most complex business problems. And when someone joins a company as a data scientist, s/he expects to work on relevant projects. But these misleading job description and companies posing as a core data science company are affecting a lot of talents.
If you look at the industry, there are people who are getting offered or they are accepting data science role based on the job description and later they are realising the job is more of data analysis or data engineering role or something else.
Furthermore, when a candidate after completing his/her data science course joins a company that has posted a completely misleading description, it not only brings the candidate’s morale down but also affects his/her portfolio.
Word to the wise: Understand the difference between all the other job roles from the domain. Just because there is “data” in the job description the doesn’t mean it is going to be a core data scientist role. A data analyst is different from a data engineer — their roles, responsibilities, tools etc. differs.
It’s quite obvious that many data science professionals would be frustrated by companies posting job descriptions that later turn out to be almost a hoax. Every company has the right to make the best out of the data science domain; however, that doesn’t mean they would do it at the cost of someone’s career. While it seems that this kind of things are still going to happen, data science aspirants can do the necessary research before accepting a job offer. Also, even if you have joined a company and you are not sure whether the company is actually a core DS/AI company, you can always refer to our articles that are focused on guiding candidates make the correct choice.