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Despite all the math and programming, data science is more than just analyzing data and building models. When you boil it down, the key objective of data science is to solve problems.

The trouble, however, is that at the outset of most data science projects, we rarely have a well-defined problem. In these situations, the role of the data scientist isnt to have all the answers but to ask the right questions.

In this article, Ill break down 5 questions every data scientist should hardcode into their brain to make problem discovery second nature.

When I began my data science journey in grad school, I had a naive view of the discipline. Namely, I was hyper-focused on learning tools and technologies (e.g. LSTM, SHAP, VAE, SOM, SQL, etc.)

While a technical foundation is necessary to be a successful data scientist, focusing too much on tools creates the Hammer Problem (i.e. when you have a really nice hammer, everything looks like a nail).

This often leads to projects which are intellectually stimulating yet practically useless.

My perspective didnt fully mature until I graduated and joined the data science team at a large enterprise, where I was able to learn from those years (if not decades) ahead of me.

The key lesson was the importance of focusing on problems rather than technologies. What this means is gaining a (sufficiently) deep understanding of the business problem before writing a single line of code.

Since, as data scientists, we typically dont solve our own problems, we gain this understanding through conversations with clients and stakeholders. Getting this right is important because, if you dont, you can end up spending a lot of time (and money) solving the wrong problem. This is where problem discovery questions come in.

6 months ago, I left my corporate data science job to become an independent AI consultant (to fund my entrepreneurial ventures)

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5 Questions Every Data Scientist Should Hardcode into Their Brain - Towards Data Science