We Don’t Need This Many Data Scientists

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I have held the title of data scientist in two industries. I’ve interviewed for more than 30 additional data science positions. I’ve been the CTO of a data-centric startup. I’ve done many hours of data science consulting.

With that background, you will hopefully realize that I’m not a data denier. I’m a firm believer in the power of statistics, machine learning, and all the tools in a data scientist’s toolbox. I know that data science is a powerhouse field filled with amazing people that are changing the world.

That being said, many companies don’t need a data scientist.

No, that wasn’t strong enough. Let me try again.

The vast majority of companies that are looking for a data scientist don’t need one.

Of all the companies I’ve worked or interviewed with as a data scientist, I’d say 80% of them were looking for the wrong role.

Some of them just needed a data analyst. Others needed a data engineer or a data architect. The rest didn’t have a data need at all.

What Problem Are You Looking to Solve?

I always ask this question when someone is looking to hire me. Originally, I asked what they were looking to do with their data, but I’ve since realized that the answer to that latter question doesn’t matter. The focus needs to be on the problem, not the solution. Companies hire to solve problems.

Good companies don’t hire a position because it’s trendy to have around. They hire because — for every dollar that employee costs them — they are getting more than a dollar in return. It’s that simple. It’s all about ROI.

All companies understand that when it comes to positions like accounting and sales because they know how ROI works for accounting or sales. They know what problem needs to be solved and they know who can do it.

But data confuses companies. It especially confuses older companies, but startups are not immune. We’ve all been told that there’s gold in them thar data.

And who doesn’t love a good gold rush?

Just like the gold rush of old, most people don’t know where to look for the gold, many of them have fallen for fool’s gold, and no matter how much a vein has been picked clean, people keep coming back looking for scraps.

The underlying issue is that companies have been told their data is valuable. And it might be. But whether packaged for sale or used internally, data is a part of a solution, and every solution’s value is determined by the cost of the problem it is solving.

Without a problem, a solution is just an idea. And, as I’ve mentioned in multiple previous posts, ideas are worthless.

Data rushes happen because companies have a solution — data — and they are looking for a problem to apply it to. It’s a completely backward approach. You don’t decide to use screws because you have a screwdriver handy. You decide to use a screwdriver because you need to tighten a screw.

Data is a resource. So why is data not treated like any other resource?

Data is inherently different than other resources in one important way.

Let’s look at oil, a pretty standard resource. Unless you are The Beverly Hillbillies, you don’t just find oil lying around in your backyard. If you have thousands of tons of oil, you have it because you planned to have it for a specific purpose. And once you use it for that purpose, it’s gone.

But companies have exabytes of data. Maybe they had it for a purpose. Maybe there was a regulatory requirement for them to keep it. Maybe it was just easier to keep than to throw away.

Whatever the reason, they have it now, and they want to use it. They just don’t know what to use it for. And they often assume data scientists are the answer. After all, data is right there in the title, and scientists are smart.

S-c-i-e-n-t-i-s-t Is Not How You Spell Engineer


Let me give these companies the benefit of the doubt and say they actually do have problems that their data could solve. That still doesn’t necessarily make hiring a data scientist the correct next step.

Data scientists solve puzzles. They take billions of pieces of data and turn them into a single, cohesive picture. But they can’t do that if you don’t give them all the pieces.

If your data streams into ten different systems that don’t talk to each other, you are setting your data scientist up for failure. You need someone that can bridge those systems, bringing the data into a single place. That’s the job of a data engineer, not a data scientist. Depending on the situation, you may also need data architecture, data modeling, and database administration.

If you really want to, you can find a data scientist that can handle everything from the engineering to the DB admin work. I’ve been that data scientist. But my rate was much higher than what they would have paid to just hire the correct person for the job.

Why did they overpay? Because they didn’t yet understand the current status of their data or what a data scientist actually does.

Why did I take the job? Because I was too naive to know better.

Everyone would have been better off if the company had hired a data engineer, waited 6–12 months, then brought on a data scientist when they were fully prepared.

Ready? Have an Aim? Hire!

Has your company identified problems that you need data science to solve?

Is your data in a state that a data scientist can work with?

If you answered both of these with a definitive ‘yes’, then you may need a data scientist. Congratulations, your company is doing things right. Pat yourselves on the back no more than three times then go do some amazing things.

If you answered either question with a ‘no’ or a general look of confusion, then save your money and a data scientist’s sanity by taking down that job posting you just put up. Maybe replace it with a posting for a data engineer or data analyst. Or maybe just be happy not to have to go through the hiring process.

Not sure what you need? Talk to a data consultant before you waste your money.

Like this advice? Take 0.001% of the money you just saved and buy me a drink someday.

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