The Specialized “Data Scientist” Will Win in The Long-Run

Original article was published by Kurtis Pykes on Deep Learning on Medium


The Specialized “Data Scientist” Will Win in The Long-Run

Becoming a Specialist Will Take You Further than A Generalist

Photo by Bermix Studio on Unsplash

In my last post, “The importance of Branding in Data Science” I mentioned that Data Science has become too broad hence branding is an excellent tactic that may be employed to overcome the noise of being a “Data Scientist” which in turn works in our favour during the vetting process.

After pondering on my own writing for some time, it made me wonder…

Would it of Been Easier If I just told people to specialize?

Despite the identity crisis we are facing as a community, I am usually not one to care about titles. However, I understand the importance of distinguishing between roles, and on that basis, I wouldn’t be surprised if we begin to see roles like “Statistical Modeller”, “Natural Language Processing Engineer”, or “Computer Vision Engineer” — Maybe not these exact names, but you get my gist — popping up, whilst roles like Data Analyst get to reclaim their identity.

In other words, the Data Science buzz has run its course and it’s time to specialize. Here’s why:

It’s Easier

There are a lot of things that go into becoming an indispensable “Data Scientist” including staying up to date on the newest trends, the best practices, and developments. Given the broadness of Data Science, having to remember all of these things across the board is like fighting a losing battle. You are better off losing that battle in order to preserve your resources to win the war.

Staying up to date with Data Science across the board is like fighting a losing battle. You are better off losing that battle, so you can focus your attention on winning the war!

This sparks the question… What is the War? Great Question. Having longevity in your career, being indispensable, being future proof.

We are all aware of how fast technology changes, hence by specializing we can lighten our load because we reduce the learning curve. Generalized skills require a lot more work, and it exerts a lot more energy because we don’t have finely honed skills. Alternatively, by honing in on one niche (or two) you make it easier to focus your time and attention, in-turn resulting in better comprehension and expertise.

It is easier to create a study plan to become a strong NLP practitioner than it is to create one for the scope of Data Science, so why not just do that? Becoming an expert generalist will take too much time, of which at the same time one can develop expert skills and branch out.

The Black Sheep

I’ve already mentioned my last post “The Importance of Branding in Data Science” and how it provoked me to write this one, but let’s put more emphasis on how it will help if you were to make the transition today…

“When there is lots of competition, it pays to stand out”

Harvard said Data Science will be the sexiest job of the 21st century and now everyone wants the sexiest job. This is in no way a bad thing in my opinion. I want as many people as possible from various backgrounds involved with Data Science, however, where people are flocking, it pays to stand out.

Remember the classic question: “If you had a heart problem, would you prefer a doctor that deals with various problems operate on you over a specialized heart surgeon?”. Probably not!

We’d probably do whatever it takes to have a specialist over a generalist, even if it means paying unthinkable sums of money. This is not to say that you should become a specialist purely for financial gain. Nonetheless, your depth and knowledge will serve as trust and credibility for your customers who we know prefer specialist — The financial gain is purely a by-product.

“Your advantage is your depth and knowledge you have in a certain area.”

Let’s rejig the question: “Would you rather someone specialized in computer vision to work on your image classification problem over someone with generalized knowledge?”


Okay, you decide to specialize in something. Say, Computer Vision! You’ve created your study plan and have decided on a cool project you want to take on with the skills you are learning.

You will become better faster because you can put all of your energy into developing your computer vision skills, and when you are good at something you enjoy it more, therefore you will want to do it more…

Source: Giphy

Lingering in the realms of “It’s not my job, It’s what I love doing” is the place we all want to be. It derives greater personal and professional satisfaction when you are good at what you do which makes you want to do that thing more — “Time flies when you are having fun”.

Thereby, specializing will develop you into a more efficient and effective practitioner. If you are spending less time doing what you do, you have more time to do other things you’d like to do!