There are so many reasons why data scientists are quitting their jobs. The list of reasons could differ depending on individual experiences. This article has therefore put together 5 important reasons why data scientists are leaving their jobs.
Variance in reality when practicing against initial expected results
The illusional perception many junior data scientists have before entry into the career path is that everything is rosy. Data Science was thought to be concerned with the ability to solve less ambiguous issues with "easy to use" machines and acquiring more knowledge on algorithms that makes significant impact on businesses. This perception actually made data scientists feel "on top of the world" but this isn't really how it turns out to in most case scenarios. In addition to this, the restriction on the ability to impact on a large scale or outside your company is another strong issue. Every individual wants to soar high and expand his/her tentacles.
Although companies differ but if it's a type of company which core business has nothing to do with machine learning; its most likely that your job as a scientist would be centered or limited to only the company you work for.
A very small percentage of data scientists get lucky enough to handle high profile projects that gives high value and versatility but this is very uncommon as well.
The demanding nature of the job
One of the prominent aspects of this job is that you'll be known as the database expert but, it can be overwhelming sometimes. The ability to navigate through all data related issues involving machines such as; Hive, Pig, SQL, A/B Testing, Hadoop, Spark, Scala is not a child's play but you have to because you risk the chance of your colleagues and executives who think highly of you. With this in mind, you would ultimately need to live up to expectations even when it is not very convenient.
This would also result in the quest to always be "on top of your game" as data scientist to improve your skill by learning new machines and other things academically and professionally, to stay updated at all times. This means you would often need to go out of your way for others to your own detriment. This won't be pleasant all the time and can be mind bugging.
Incommensurate Compensation
Salary is one of the primary reasons and motivations for the entry of this career as a data scientist. Generally, no one enjoys a poor or incommensurate pay-check!
According to recent statistical reports from Glassdoor and the likes of them; they have shown appalling average salaries for data scientists. This has become more frequent as data scientists whose work is outstanding in their designated fields are normally nabbed by high profile companies who offer extremely inflated salaries, while the middle class and small companies offer more. This is so inappropriate as the reverse becomes the case.
There is need for some standardized and commensurate compensation plan for data scientists, in order to encourage them stay in their jobs. There's also a significant need in differentiating the salary of a data scientist without experience with that of an experienced professional. This should be taken seriously as noncompliance to this can lead to redundancy in a company's man-power and unsatisfied job delivery even by an experienced staff.
Political implications
In reality, data scientists believe that having knowledge of many ML algorithms will make an indispensable asset. Professional and skilled data scientists can be saddled with ad hoc work which includes extraction of figures from databases and the ability to undertake some projects. It is a very vital aspect of their job, hence it can also be backbreaking for them.
Working in Segregation
Unfortunately, not all data scientists have the luxury of initiating their own projects independently in the bid to solve a problem. In this case, data scientists who work in segregation will strive to impact great value! Whenever good data products are seen with the touch of an expert design interface with brilliant abilities, it is usually assumed by the users to solve problems. In practice, it usually entails vast skills to be assembled or brought together by a good number of data scientists.
Ideally, it is not advisable for a data scientist who works in segregation to undertake such projects as this will result in ultimate failure or even if the project has a probability of becoming successful, its tendency is very low and would also be time-consuming and demanding.
In conclusion, the ability to be an efficient and effective data scientist involves knowledge of political and hierarchical work in business. Working for a firm that share same values and critical paths with you as a data scientist should be a non-negotiable goal that will be satisfactory to your career, personal growth and development. It is pertinent to also keep an open mind regarding expectations from the job role as it could differ in various companies.
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