Why you should be a Generalist first, Specialist later as a Data Scientist?
By: Admond Lee
Tags: Data Scientist, Towards Data Science, Learning, Data Science, Education

So what’s a Generalist and a Specialist?

Before going any further, let’s first understand what we mean when we talk about being a generalist and a specialist in data science.

A generalist is someone that has knowledge in many areas whereas a specialist knows a lot in one area. Simple as that.

Particularly in data science, it’s notoriously hard to become a generalist in all phases of data science project lifecycle. It takes years to acquire all the skills in different areas, yet it’s not necessary to master all of them.

Similarly, it’s not easy to be a specialist in data science either.

Now you might define a generalist as the Jack of all trades, master of none.

I couldn’t agree more about that.

And this is precisely the reason why I’d choose to be a specialist in the later stage of my data science path.

Why be a Generalist first?

After all, being a generalist is not meant to master anything, but rather to understand the full picture of the whole data science project lifecycle.

The question is: Why is understanding the full flow of the data science project lifecycle important in the first place?

You see. As a data scientist, we don’t build a machine learning model just for the sake of building it. We first understand the business problem and frame that into a problem that can be solved through data science approach. Then you need to identify data sources and define success metrics. Depending on your company’s stage of maturity, you might also need to build a pipeline to collect data (YES, you may not even have data in place)…

We can still go on and on but here is the point — all the job scopes above are part of being a generalist. The good thing is that you’ll get to know the full picture of the data science problem as a generalist — as a data scientist in the beginning of your career.

In other words, you’ll learn and you’ll grow, tremendously.

What I’m advocating here is this: If you’re someone who is starting out in data science, my recommendation is to be a generalist first. Go join a startup and take on many hats as you will probably be the only data scientist in your company. Generalists add more value than specialists in a company’s early days, since you’re building most of your product from scratch and something is better than nothing.

Your machine learning models don’t have to be a game changer but should be able to provide actionable insights and results.

Learn how you can help the company generate more revenues. Learn how you can leverage the existing data or build some pipeline to collect data to solve some problems.

Start with the low-hanging fruit first. There isn’t always a need to go for AI if the company isn’t ready for that. In fact, normal statistical approach is typically sufficient to tackle some simple problems.

The ideal data scientist is a strong generalist who also brings unique specialties that complement the rest of the team

Be a strong generalist. Be the Jack of a trades.

Once you’ve enough experience and you’ve found your interest and passion in a specific area (say NLP), then you can deep dive into that, which leads us to the next stage.

Why be a Specialist later?

Say if you’re a NLP specialist. Your focus could be solely on building the best NLP classifier model given the data. And that’s it.

All the things are already set for you. The business problems are well defined (done by product managers). The pipeline is ready and maintained 24/7 (done by data engineers) and the data is there for collection. What you need to do is do what you’re best at. This is crucial as you can focus on your expertise and strength to add the highest values to the project.

It’s perfectly fine to be specialist in data science. Being a specialist in your niche plays an important role in a company, which is also something that makes you irreplaceable and valuable to others.

At this stage, since now you’re already experienced in different areas as a specialist in data science. Your experience and expertise are not something that can be easily substituted by others.

Even better, you’ll be able to focus on your specialization and work with others as a team with your broad knowledge and understanding of other parts of the data science workflow.