The concept of machine learning and the set of skills necessary to work in the ML area is often misunderstood. It’s often confused with business intelligence, big data, artificial intelligence, and analytics. With so many overlapping areas, it’s important to inform your target audience about machine learning before you can provide your services. Never stop explaining it to others.
Although, the use of deep learning is not necessary for solving roughly 99 percent of business problems. Linear regression, logistic regression, and cluster analysis can usually solve most problems, and other methods are rarely useful.
But there are really only two types of jobs in ML:
Look at the data and find or make the right model.
Builds data pipelines so that data can be used by downstream workflows; builds data pipelines that use the model made by a data scientist and sends the result to workflows that use the data.
But, maybe on purpose, hiring companies often mix up these two roles, which leads to the following situation in the job market. Which means they want someone who can do both jobs, so they pay someone the salary of one job and let them do both. And as you might guess, such people are hard to find. So, as someone else said above, it’s not at all competitive in this way.
Don’t let those high-tech companies’ impressive ML/AI use cases fool you. Most companies are just starting to use AI/ML, so the most important job for them is a data engineer. You need this role to set up your data infrastructure so that data scientists can use the data.
So, many companies hire data engineers, even when they think they need a data scientist. Since a data engineer is a software engineer who specializes in the data/big data field, this job is very popular on the job market.
But when it comes to the role of a data scientist, things are tough because most hiring managers only care about getting the work done, not getting it done right. They think that a data engineer can replace a data scientist by using ML libraries.
Unfortunately, this is true for most simple use cases. You just need to use some simple linear regression, time series model, or simple NN to get most of the work done well without knowing if the model’s assumptions are met so the result is valid. This makes them think that it’s not important to know the basics of AI and ML. For example, you don’t have to understand physics to use a drill.
On the other hand, most of their use cases are really simple, so you don’t have to invent new things or do a lot of research to get a model.
You just have to try, make a mistake, and try again until you find a good one. Even if you have a great idea for R&D, they don’t like it because they just want to keep doing what they’re doing. They don’t want to spend money on R&D ideas that are likely to fail 99% of the time. If a company wants to hire a data scientist but isn’t interested in R&D use cases, they probably want a data engineer.
Since only big tech companies really need data scientists, there isn’t a lot of demand, but there are a lot of people who can do the job.
The data engineer role is not competitive, the data scientist role is very competitive. I don’t want to talk about the MLOps role because it’s just the same person who has to do the two roles above but only gets paid for one of them.
Even without the excitement that sometimes comes with the hype, it’s still a fun place to be. Over time, real results speak for themselves, so if you want to make a career in this field, the best way to protect yourself is to stay up to date on trends and hype, and when you get clients, make sure they are happy.
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