Why data scientists leaving their job is becoming increasingly common?

You may be taken aback knowing that “Data Scientists” – professionals who are receiving massive income, entertain thoughts of quitting their job. Nearly 80-90% of data scientists spend 1-2 hours of a week searching for a new job. It’s shocking but true. What makes people quit a job that is touted as the dream job of the 21st century? What dilutes their interest in solving complex problems through machine learning algorithms? Well, here are the answers.

1. Expectations go wrong

Every data science professional loves a challenge, i.e., the challenge of solving difficult problems that exist in a business environment. They dream of offering real-time solutions via their efforts. But sadly, many companies are yet to develop the infrastructure which allows them to make full use of AI. And, a majority of these companies doesn’t form a proper data think tank first before hiring newbies. Therefore, a large amount of the data scientist’s energy goes into sorting the shortcomings associated with the reasons mentioned above rather than writing intuitive machine learning algorithms to help make a difference.

2. Winning the favor of influential people

In several companies, the role of a data scientist isn’t defined entirely by his knowledge about machine learning algorithms. They also have to work constantly towards meeting the requirements of people with authority. If a person with a superior designation needs a data practitioner to fetch numbers from a database, he/she has to oblige to create a good impression. It is an essential part of their work. As a result, it makes them think whether it does justice to the talent they have. Again, it comes down to the expectations they had initially.

3. Misconception from employers

Not many employers know what exactly a data scientist does. Often than not, they assume a data scientist knows every single thing about data, and turn to him/her to prepare analytics report and other database related works. Further, he/she is expected to know the ropes of technologies like Hive, Pig, Hadoop, SQL, Python, Scala, MySQL, Neo4J, Tensorflow, and anything based on machine learning. So, working in an environment where they can’t exactly clarify what their expertise is, what can be achieved by them and what not, hampers their morale big time.                                                                                                                                       

4. Fighting a losing battle

The output which a data scientist is expected to deliver involves a whole lot of technical skills. And, practically, it is hard to find one who can do the job single-handedly. So, if a company, assigns a project to a lone data scientist, without setting up a proper data ecosystem, he/she is likely to fight a losing battle, eventually walking away from the job.

The bottom-line

With everything said, to be an excellent data scientist, you have to find the right company that operates in line with the aspects which you consider are vital for career growth. And yes! It is not easy finding such a company at the outset. Over and above, you should not go overboard with the expectations too.

In case you are in pursuit of discovering the ideal companies to leverage the expertise, have a look at this list of employers in the data scientist jobs website.