Why many Data Scientists Lost Their Jobs in 2019

Swetta Jha
Nov 1 · 3 min read

As one of my friends is a Data Scientist, and you have read the title correctly, but someone had to say it. We have read many Stories, about Data science; it is being the sexiest job of the 21st century.

Some attractive, sums of money that you can make as a Data Scientist. That it looks like an absolute Dream Job. The factor in that, the field contains an abundance of highly skilled people, that geek out to solve critical problems. We have everything to love, about the Job.

But the Truth, behind Data scientists typically “ spend 1–2 hours per week, that looking for a new job. As said in an Article, that machine learning Specialists, topped its sequence of developers, who told, they were looking for the latest Job.

At 14.3 %, Data scientists were a close second, at 13.2%. These Data was collected by Stack overflow, in their Survey Depended on 64,000 Developers.

My friend has been in a Position and has recently switched, data science Jobs by myself.

So, why are many Data scientists, look for new Jobs?

Before I answer this question, I should clarify, that my friend is Still a Data scientist. On the complete, I love this Job and I Do not need to change others, from aspiring to be like a Data Scientist.

Because it will be more fun, that stimulate and reward, the main aim of this article is to show some negative aspects of the Job.

From point of perspective. We have 4 big reasons, that why I think many data scientists are not, satisfied with their Jobs.

Expectation Do Not Reach Reality

Big Data is like Teenage Love, Everyone talks about it, but no one really knows, how to do it. Everyone assumes, everyone doing it, so everyone claims, they are doing it.

The above Quote is very Adaptive, many Junior data scientists, that I know, wanted to get into data science, because it was totally about solving some complex issues.

With some cool, machine learning algorithms, that make a big impact on a business. This was a little chance to feel like, the work we do, which is important, than anything we have done before.

Not to mention, this every time, not the case.

In my point of view, the fact that expectation doesn’t match reality, it is the ultimate reason, that why Data scientist leave.

There are many reasons, for this and I cannot possibly come up with an exhaustive list. However, this Article is very essential, with a sequence of Reasons, that I Faced.

Politics

Politics show the practice of Governance, or some other actions, that target on getting the position. The policy is a big Plan that you put in Practice after you have some governing position.

The policy is a big plan that you keep in practice after you have got governing Power. I know this looks like a waste of time, coming from political scientists.

Correct Policies are Required in point to become, a functioning data science team. However, how are you going to keep them in place, that if you have a problem?

How do you get power, with politics and you maintain it with other politics and policies?

Working on isolated Projects and Teams

When we look at successful data products, we every time see expertly designed user Interfaces, with Intelligent capacities and most Important.

A useful output, which at very least, is perceived by users for solving a pertinent problem. Now if a data scientist spends, their time, that only learning How to execute and Write machine learning Algorithms.

You are meaning to anything about Data

By following, on from doing anything to please, the correct people, those same people, with all of the clout, every time don’t understand, what is meant by “data scientist”.

This means, that you are the analytics expert, as well as the go-to reporting, guy and let’s not forget, that you are the database expert.

It is not like, non-technical one, executives that design, many assumptions about your skills.

So to be an effective data scientist, in Industry, it does not suffice, to do well in competitions and full some online courses like Data Science Online Course.

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