Data Scientist vs Machine Learning Engineer

Two job roles that have been gaining a lot of popularity in recent years are data scientists and machine learning engineers. They share a lot of similarities and making sense of the differences can be confusing this post will try to help you with that.

So, what is the difference between machine learning engineers and data scientists? Machine learning engineers will generally take be responsible for making machine learning models that are useable at scale. Data scientists will generally be responsible for analyzing data, helping with business decisions and, usually, for building machine learning models.

The roles of both positions are still being defined and their roles will often overlap with each other.

Data scientist

Depending on the company, the role of a data scientist will vary a lot and, sometimes, the role of a data scientist will be very similar to the role of a machine learning engineer.

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What they do

Generally, the role of a data scientist will be to analyze data from various sources to help get a better understanding of how to improve aspects of the business. Data scientists will also, usually, be responsible for data cleaning and pre-processing the data, deciding on and applying machine learning models to the data and explaining their results to invested stakeholders.

Depending on the company, data scientists will either be focused mainly on analytics and building data pipelines to help make business decisions or they will focus on building machine learning models.

How much data scientists get paid

According to Glassdoor, the average salary of a data scientist, in the US is $117,345.

The actual salary that you could expect to receive will depend largely on where you intend to work and your experience level.

According to PayScale, the 10th percentile of data scientists in the US earn $62,000 per year and the 90th percentile earns $131,000.

Skills and background of a data scientist

Data scientists will normally have a strong background in mathematics and they will specifically have a good understanding of calculus, linear algebra, probability and statistics. They will also have a strong understanding of programming, normally in Python, database software and data analytics.

Typical degrees that data scientists will have will be quantitative in nature and will include the likes of computer science, mathematics and statistics. Often, companies will also expect them to have a masters degree as well but there are many companies that will be willing to take on candidates, with a bachelors degree, that can show sufficient experience.

How to become a data scientist

To become a data scientist it will be necessary to have at least a bachelors degree in a quantitative field.

It would also be extremely beneficial to be able to get some data science internships as an undergrad or as a graduate student but it is not absolutely necessary.

In addition to that, it will be necessary for you to show that you have experience in machine learning and data science. Ways that you can show that you have experience in machine learning and data science would be to create machine learning projects that go through the whole data science process, do machine learning competitions, write machine learning blog posts, create machine learning youtube videos, contributing to opensource or going to hackathons.

I have given more detail about how you can go about getting a job in machine learning, in the past, here.

If you need to learn the skills to become a data scientist then I will give you some links to courses in the various subjects needed to become one below:

Calculus (MIT)

Probability (MIT)

Linear algebra (The University of Texas at Austin)

Machine learning (Andrew Ng, Stanford), Deep learning and machine learning (MIT)

Data analysis (Data School’s channel on Youtube)

Is machine learning required to become a data scientist?

It will largely depend on the company. At some companies, the job title “data scientist” really means data analyst where a familiarity with machine learning is all that is necessary. However, at most companies, data scientists will be involved in building machine learning models in some way so it will usually be necessary to have a good understanding of machine learning.

Machine learning engineer

The role of a machine learning engineer will usually be to build machine learning models and to put them into production at scale. This means that they will use data to train machine learning models which will then predict things such as what a customer is likely to buy or what a person is likely to watch. They will then take these models and make them work at scale and usually in real-time.

In some companies, the role of the data scientist will be to build the machine learning models and it will be the job of the machine learning engineer to take those models and make them work, at scale, in a production environment.

How much machine learning engineers get paid

According to GlassDoor the average machine learning engineer salary is $121,707.

As with data scientists, the pay that you can expect to get will depend largely on where you want to work and your level of experience. In San Francisco, you’ll find much higher salaries but the cost of living will be much higher as well.

According to PayScale, the 10th percentile of machine learning engineers make $76,000 and the 90th percentile makes $152,000. This shows that machine learning engineers will usually make more than data scientists. However, many of the jobs listed as data science should really be listed as data analytics. This will likely make the average data scientist salary seem lower than it actually is for data scientists that must develop and build their own machine learning models.

Skills and background of a machine learning engineer

A machine learning engineer must have strong knowledge of the machine learning algorithms. They will also usually need to have a good knowledge of computational complexity, especially for the interviews. They will need to understand calculus, linear algebra, probability and statistics. They will also need to have software engineering skills since they will often have to output their models in the form of software as a part of the companies larger ecosystem of products.

Like data scientists, machine learning engineers will normally have a masters degree in a quantitative field such as computer science, statistics and mathematics.

How to become a machine learning engineer

To become a machine learning engineer it would be necessary to get a bachelors degree in a field such as computer science and usually a masters degree as well.

While doing the bachelors degree and the masters degree it would be very helpful to do internships in machine learning and to build your own machine learning projects.

Some ways that you could go about getting interviews and some exposure would be to go to hackathons, email recruiters, contribute to opensource projects, create your own machine learning software to create a machine learning blog or youtube channel.

You can watch the video below to see an interview with an Airbnb machine learning engineer.

Should I be a data scientist of a machine learning engineer?

Which one you should be would be largely down to personal preference. If you prefer to spend more time analyzing, preparing data and communicating your findings among others then you would probably prefer being a data scientist. If you like designing and scaling the machine learning algorithms, in a production environment, then you would likely prefer being a machine learning engineer.