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Do many people applying to Data scientist positions end up either unemployed or in other positions because of the intense competition for most data scientist positions?

Posted by StepTb su ottobre 6, 2018

If you’re good at automating stuff via programming you’re never going to be unemployed.
Economic growth nowadays comes in large part from increasing productivity, which is done by automating.

If you’re good at analyzing and interpreting data, same thing.
Data is the equivalent of oil in the digital economy.

What you’re probably referring to is a specific type of position that matches the classic description of what a DS does that you can find posted online everywhere.
Well, the problem with that type of role is not exactly the competition. You’re going to find it difficult to get a position like that even if your skill set is perfectly aligned. The problem is that the companies owning very large data sets that also have a specific strategy about how to leverage them are still a low number, and for the moment are either American or Chinese.
You need to get hired by one of those companies in order to get the experience needed to reach the type of DS role you have in mind and articles on the interwebz love to talk about.

But why obsess over that when the same skills can get you so many easily accessible alternatives?

Annunci

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Why is becoming a data scientist so difficult?

Posted by StepTb su ottobre 6, 2018

It’s like asking why is becoming [any other high-level profession] difficult. DS is not an entry-level position, and I’m constantly surprised by how many here on Quora or elsewhere seem to think it is.

Let’s leave the theory alone, since the typical answer focuses on that.

The real mountain to climb is not learning theory, which is the starting point and can be done on your own, but developing as an applied programmer + developing a domain knowledge. Having all three is the only way to reach a DS position, because otherwise you’re not going to be useful there. You’ll just sit on a mountain of data and stare into the abyss, but that’s what the folks who accumulated that data are already doing, and they don’t need you for that.

For the first part, there’s no shortcut – you just have to practice constantly and challenge yourself with progressively complex issues to solve *in a real setting*. When you come out of academia you have no clue about this. And you won’t be able to do it in your room. You’ll need a work environment where you’ll be given real datasets and you’ll deal with real problems you’re expected to solve. This way you’ll be able to develop an eye for practical solutions.

For the second part, forget about shortcuts either. Developing domain knowledge is fundamental to understand what questions need to be asked, and the only way to reach that point of awareness is to work in a specific field and understand where exactly the well-known issues typically are, where the possible points of optimization are, and what ideas are actually still unexplored (and not new and cool in your head, but old news outside of it).

Previous points can take easily 10 years of work experience, and, even if there’s a fast progression in challenges and complexity, 5 years bare minimum.

Finding a place where that progression happens and you’re finally able to reach the point of being useful in DS is what’s really difficult.

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