You don’t need to become a Data Scientist to work in AI

I often get asked the question, how do I break into AI?


Afke Schouten

3 years ago | 2 min read

Here’s one way to do it.

Many people want to work in AI, and most that start off their journey aspire to become a data scientist. Apparently the sexiest job of the century.

The first thing they do is a Coursera course and then start working on Kaggle competitions, followed by trying to find a job as a data scientist. If this is you, you are one of many, and it will be hard to stand out of the crowd.

Whilst understanding AI through a Coursera course is not a bad idea — you need to educate yourself and become AI Literate — data science is only 5% of the work and many other roles are available or will become available in the future.

So what else could you do?

Look into your own job or department

One way to break into AI is to look at your own job. Perhaps all or parts of your job can be enhanced or replaced by AI. Are you working with data and is this data used for making decisions? Even small decisions such as categorizing a new customer can be enhanced with machine learning.

Identify these decision points and kick off an AI project within your company. You will be the ideal business stakeholder for a data science team.

Let’s assume you work in marketing. You can start thinking of the data you collect on your different customers. How are you making decisions who you are reaching out to, and when? Are you already collecting data on who is responding to your marketing efforts? You can start building the basis for the data, and the business case.

Look into your industry or domain

Look at companies that already work in AI and need your skills or industry expertise. Industry and domain knowledge are key to the success of AI projects.

There is a large AI literacy gap on the business side and when you have educated yourself in the basics and can bring the desired domain or industry skills to a company you will be a huge asset.

Let’s assume you work in the energy industry. Great, there’s a lot of potential for the application of AI in energy. And if not now, many companies will start looking into AI in the next few years.

Collect different potential use cases in the energy sector, predictive maintenance, energy trading, etc. Work your way into understanding these use cases and you’ll be a great asset for your own or a competitor company!

The key to this story is, stick to what you know and are good at, and grow your skills from where you are strong.

Build the bridge from the business side, this is where you’ll be successful.

If you want to learn to understand machine learning on the side, please do so. But there is no need to push yourself to become a data scientist.

In fact, I truly believe that in the longer-term future many roles in AI will be non-data science roles. And maybe it will just require some patience to move to the right role, but that doesn’t mean now is a good time to start preparing for it.

This article was originally published by Afke schouten on medium.


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Afke Schouten







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