Why to Hire Machine Learning Engineers, Not Data Scientists

Machine Learning Engineers finally deliver on the promise of AI.

Many companies hired the wrong people to build AI products. Now they’re course-correcting by looking for machine learning engineers.

Where did we go wrong?

In 2017, the hottest AI job was “data scientist” – a statistician who can code. The idea was that companies could finally build AI-driven products with the help of PhD statisticians. 

Finding and hiring data scientists was hard. But even worse, things didn’t work out as planned: 18 months later, many companies ended up with just Proofs of Concept (PoCs) that would never make it to production.

Two big mistakes got us here:

1. Conflating research and business skills: There are two kinds of machine learning: academic (data scientists) and practical (machine learning engineers). To succeed in building business applications, you need people who are well versed in the practical.

2. Treating machine learning like a black box: Since there was no widespread understanding among managers about what machine learning is, data scientists were left to figure it out for themselves. This was a big mistake, because successful ML projects require a tight feedback loop between domain and data knowledge.

AI Researchers are like electrical engineers, while Machine Learning Engineers are like Cooks.
There are two types of machine learning skill sets: AI Researchers that develop new algorithms. And ML Engineers that use algorithms to build AI business applications. In this analogy: If you want to serve food, you need cooks (ML Engineers), not electrical engineers who build appliances (AI Researchers). Image Source: Author

Enter the machine learning engineer

What many companies didn't accept in the beginning is now becoming clear: You don't need specialists (statisticians) to build machine learning solutions. You need generalists – experienced engineers who understand how to use AI and are also excellent communicators.

But wait isn’t that like searching for a unicorn?

Not really, and here’s why:

Using AI software is simpler than you might think  – You don’t need a university-educated specialist to do it. It’s just a tool that any motivated engineer can pick up – similar to front-end development, or databases, or scalable cloud deployments. It's a skill, not an academic discipline.

Finding these people is easier than you might expect – AI + Engineering + Business Understanding = Mythical Unicorn? Not quite. Machine learning is a hot topic. Many engineers already understand it well, and many more want to get into it. The tougher challenge is finding the great communicators.

The 3 skills you need to look for

The 3 skills machine learning engineers need – Source: Author

Now that we’ve clarified those misunderstandings: What are the standard requirements for a machine learning engineer?

1. Solid development experience

The problem that doomed most AI projects was a lack of good engineering practices. Your ML engineer should know how to to build, improve, and maintain business applications.

2. Machine learning basics

The truth is, an experienced, motivated engineer can learn to build AI applications in 1–2 years. It would take a data scientist much longer to become a good engineer (2–4 years on average).

This also means that if you have time and engineers, you can grow your own machine learning team. No hiring needed.

3. Good communication

AI is over-hyped and novel, so many managers still need to get to grips with it.

This means communication is a challenge. So your machine learning engineer needs to be a bit of a project manager, ensuring that what the company wants matches up with what’s being built.

In our experience, more engineers than you might expect do very well in this role – once you give them a chance.

Help your ML Engineers succeed – learn about AI yourself

Only if the whole company knows the basics of AI, can the ML Engineers be effective
ML engineers need input from a team that knows the basics of AI. Source: Author

To sum up, companies that want to take an AI application from idea to production mainly need machine learning engineers. These engineers can take a business idea, identify an appropriate approach among hundreds of research papers and open source software options, test it, improve it, and – crucially – take it to production in the form of sound, reliable software.

We left out one thing: Engineers can only learn so much about your domain in a given amount of time. To gain the crucial insights that make an AI solution valuable, they fully depend on you.

So the rest of the company needs to learn about AI too. Then you and your team can come up with workable ideas, give the ML engineers helpful input, and create realistic expectations.

We follow the same strategy

This is the approach we follow. Everyone on our team is a machine learning engineer. If you search for a new apartment, book a holiday, or order a Mercedes Sprinter in Germany then you’ve probably already come into contact with one of our machine learning solutions.

Reach out to us if you need help with a complex machine learning challenge.

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