Here’s why are data scientists leaving their jobs

Off late there have been countless articles and blogs where the talk for the world is that data scientists quitting their job. In fact, this has been shared in an article by Havard University as well. I know a lot of you are wondering, how is that even possible? especially when a data scientist is making handsome money annually this seems pretty imaginary.

Professional who have been following data science should be knowing this that this industry is in abundance high skilled professionals and each one of these professionals are enthusiastic about how to solve complex problems. However, the truth is that 13.2% of the data scientists are spending around 2 hours a week looking for a new job.

In an article by the Financial Times, it was found that machine learning specialists are topping the list in checking out new jobs where the percentage value for this is 14.3%. (This data is a mirror image of 64000 developers)Why are data scientists looking for new jobs? Here are some reasons why data scientists are dissatisfied with their jobs.

1.Expectations Vs Reality:

There are countless Junior data scientists who want to become a data scientist however it doesn’t happen in the right time for him/ her. This typically is because of their expectations going wrong. When a professional join an organization at the position of a junior data scientist the typical work image that they have is that they will be working towards solving complex problems with cool and sophisticated machine learning algorithms that add on the businesses. Now, these are some tools that make a professional feel that they are contributing to the organization on a huge scale. However, this often is not the case.

In my opinion, the reason why so many data scientists quit their jobs that often is the there expectations overshadowing the actual job. In fact, the lack of infrastructure in organizations is also a major reason for someone to quit their job. In the industry not all but there are many organizations which lack the infrastructure that a data scientist will require to work upon without which the work just adds up on every single member of the team that otherwise would have barely taken the team few minutes. Lack of infrastructure has contributed to the cold problem in AI.

This is one of the most crucial reasons why organizations cannot hire senior data scientists who have the experience. Thus whomsoever is required by the organization for a role like such is likely to write smart machine learning algorithms to drive insight but obviously can’t do this because They are busy sorting out the data. In fact, professionals who are stubborn enough are busy creating a list of the infrastructure that will be needed for them to speed things up with accurate results.

This frustrates both the data scientists and the management since they are looking for results in every meeting which for obvious reason is a longer time than the usual. Thus, this affects the "two-way relationship" amongst the employer and the data scientist and this can happen either one of the two circumstances or even both. When the organizations are over expecting while it is not in the right position (typically in its introductory stage) or its goals have not been aligned with the goals of data scientists.

2.Politics reigns supreme:

No matter the industry or the organization, politics is something that just finds its way to ruin a professional’s life and their daily motivation to work. In data science, the most difficult thing is handling egos and then managing data while satisfying there is a data requirement, in fact, this even has been highlighted in various researches as well conducted by Forbes and rdisorder.eu. This is something that plays on a professional’s mind that they think about this issue more than planning their days which impacts both the business as well as the productivity of the professionals.

3.You’re data-man while you work in isolation:

Despite the fact that a data scientist is a go-to person as far as identifying the trends is concerned a data scientist is mostly found working in isolation. It is assumed that a data scientist doesn’t need to have too much support from the teams since they are majorly working on sophisticated tools. However, what these organizations or the professionals feel is that a data scientist will not be needing any further technical or regular support which truly isn’t the case.

A data scientist just has the data to prove a point. However, before they prove their point they need to figure out the reason why this issue arose in the first place. And this is something that cannot happen all by itself. Because it is imperative for the data scientist to fetch answers to all the whys, whats and hows.

Having answers to everything gives you an edge in sharing with your superiors that the situation can be controlled by the following certain changes and you can thus share with them your course of action for the same.

Being an effective data scientist doesn’t suffice one must also be politically correct first. And unfortunately, that means you cannot question the hierarchy or even suggest certain things when you know that at some point they are bound to backfire.

Finding an organization that considers these 3 points is the most unlikely thing to get in the entire industry. Thus, you need to adjust your expectations of what to expect from a data science role.