Why Data Scientists Are Increasingly Quitting Their Jobs: Lack of Skills or Different Expectations?
A data scientist’s stock is currently at an all-time high. As we betwixt 2022, there aren’t many careers that can match the mystique, glamour, and respect that a data scientist commands.
I’ve seen non-data science (or non-technical) people regard a data scientist as having superpowers. There are a variety of reasons for this, including media hype, but there’s no denying that a data scientist’s job is highly regarded.
To support these claims, I have listed down a couple of reports on the most promising jobs, I’m certain you guessed the job at the top of the list:
The aforementioned figures are jaw-dropping. From Fortune 500 businesses to Startups, organizations all across the world desire to establish a bunch of talented data science professionals. Without a doubt, the demand for data scientists is great, remunerations are competitive, and benefits are plenty, which is why LinkedIn has named data scientist as the most promising career.
Nonetheless, despite all of these encouraging tendencies, there is a nagging feeling of unease.
What is causing this? Is there something we’re missing out on?
Let’s skim through some of the major reasons why data scientists are leaving their ostensibly ideal positions.
This is among the most common problems in the world of data science. The distance between what data scientists assume and what they do in the company is expanding. A range of factors play a role in this, and they may differ from one data scientist to another. The gap between expectations is determined by one’s level of expertise as well.
Let’s use the case of zealous data scientists as an example. They are mostly self-taught and have acquired their skills from books and videos. They have limited experience with real-world applications and datasets. Personally, I’ve also met a lot of budding data scientists who had no notion about:
Freshers (and everyone else, to be upright!) are enticed by the opportunity to experiment with fancy machine learning techniques and cutting-edge frameworks.
The truth is that the industry does not operate in this manner. There are far too many variables at work for a data science project to resemble what we see in online data science events.
The company wants you to know how to process and store data, how to effectively handle version control, and how to put your models into production, to name a few crucial features. This misalignment of perceptions is a fundamental hurdle that causes data scientists to leave their positions.
To bridge the gap between anticipation and reality, I always recommend freshers and novice data scientists talk to their seniors and company alumni regularly.
Who doesn’t enjoy taking on new challenges? Given the rate at which advances are made, I would suggest that the data science profession is ideal for these issues. Consider the Natural Language Processing (NLP) domain; the quantity of changes that have occurred in the last two years is incredible.
Mostly every data scientist would jump at the chance to work on these cutting-edge methods and frameworks. Who wants to spend years creating and iterating on a concordant logistic regression model? The role of data scientists is not immune to the element of stagnation.
