Data Science Reality Check: My Predictions Come True (or, A Piece of Advice to Young Data Scientists)

Read this if you’re a data scientist or consider becoming one.

Almost six years ago, when Data Scientist was named the “sexiest job of the 21st century”, I wrote a blog post telling young professionals not to learn data science as a career move. My claim was that the data science field fill gets commoditized, and if you don’t possess deep (I mean DEEP) knowledge of either algorithms or the business you are working at, you will end up a mediocre coder.

Look what happened. Data science has indeed become commoditized in many fields. Many data-intence businesses work just fine without data scientists. Even I, a very experienced data scientist, got laid off because I couldn’t bring the company value that would justify my salary. People like Matthew Yglesias from suggest that data scientists learn how to roll a burrito or mine lithium.

Why did this happen? Well, I was right. Data science has become a commodity. Each self-respecting platform offers AI tools (I hate the term AI, by the way) such as keyword extraction, insights, predictions, anomaly detection, recommendations, and many more. Tableau, PowerBI, and even Google Sheets or Excel offer tools that were once only available through custom data and code fiddling. The Data-Science-As-A-Service niche is full of products such as and And we haven’t even started talking about the new word of the day: the GPT.

Being an experienced data scientist, people often ask for my advice and help. In the past, when this happened, I used to discuss possible custom-tailored solutions. Now, I find myself suggesting the person looking at product X or Y will solve their problems in a fraction of the time and cost. 

So, what do we have? What does all that mean?

Data science has become a commodity. In the past, to get a nice salary and a sexy title, it was enough to know what training, testing, and cross-validation were. Today, you absolutely have to know the theory and be a fast and good coder. But most of all, you must hone your communication skills and learn the business of the company where you work. Only this way will you be able to ensure your efforts are always aligned with the stakeholders and that you can consistently deliver value.

This is a career advice post. Check out the career tag and the Career Advice category of this blog.

By Boris Gorelik

Machine learning, data science and visualization

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