Machine learning Engineer vs Data Scientist

You might have heard about Machine learning, and Data Science a lot these days. These fields are becoming increasingly popular, with a number of applications in various industries, and are changing the way many things are done traditionally.

In our daily life, there are many cases, when we are using/experiencing Machine Learning, either knowingly, or unknowingly. Data Science has also been of great use, for taking data-driven decisions for businesses.

Machine learning Engineer vs Data Scientist

Data Science and Machine learning are revolutionizing many things. Remember that when we talk about Machine Learning, it is a term that comes under the bigger umbrella of Data Science.

Along with this, you might have heard about two very popular profiles, Machine learning engineer, and Data Scientist. These roles may look overlapping, with similar responsibilities, but there are some differences that we need to understand. So, in this article, we are going to understand what is Machine Learning Engineer, what is Data Scientist, and what are some of the points of difference between a Machine Learning Engineer, and a Data Scientist.

You can also read about Which is better – Data Science or Machine Learning, to know more about both these fields.

What is a Machine learning Engineer?

A Machine Learning Engineer is a professional, who is responsible for designing, developing, implementing, and maintenance of Machine Learning algorithms and models, to solve complex problems, related to various areas, like finance, healthcare, e-commerce, etc.

As a Machine Learning Engineer, one needs to have expertise in various programming languages, like Python, and R, have knowledge about Data Structures, have a good understanding of some software tools, which can be required for data analysis and other things, have a good understanding of Mathematics(linear algebra, statistics, probability, calculus), have a good understanding of different Machine Learning Algorithms, and have a good knowledge of various Machine Learning libraries and frameworks, like Tensorflow, PyTorch, and scikit-learn.

Machine Learning Engineers work on interdisciplinary teams, with other professionals like Data Scientists, Software Engineers, other members of the team, and domain experts, to analyze large and complex data, design, and implement the machine learning algorithms and models, and deploy them into the production environment.

What is Data Scientist?

A Data Scientist is a professional who is responsible for collecting, analyzing, and interpreting the data, to get actionable insights, and knowledge from the data, so that they can help to take data-driven decisions for the business. Because of this, it helps in taking data-driven decisions, which are better for businesses.

Data Scientists work on large amounts of data, and use statistical techniques to gain insights from the data,

As a Data Scientist, one has to have expertise in data analysis, data visualization, Machine Learning, Mathematics, and other software tools used in the process. They use these skills and tools to perform different operations, find patterns, and structure in the data, and take decisions on the basis of data.

So, this was about what is Data Scientist. You can well explore what is Data Scientist, and how can you become one, but here, we are more interested in understanding the points of differences between a Machine Learning Engineer, and a Data Scientist.

What are the differences between Machine Learning engineers and Data Scientists?

Now, that we are quite familiar with what is Machine Learning Engineer, and what is Data Scientist, let’s have a look at some of the points of difference between a Machine Learning Engineer, and a Data Scientist.

Machine Learning Engineer	Data Scientist
Design, develop, implement, and maintain machine learning algorithms and models to solve complex problems in different industries.	Collect and analyze data, and draw useful insights from the data. Work on large and complex data, and use statistical techniques to gain insights from the data.
Machine Learning Engineers Focus on the development and deployment of Machine Learning Models.	Data Scientists Focus more on analyzing data and extracting useful insights from the data.
Responsible for designing, developing, and deployment of Machine learning models, and working on large amounts of data.	Responsible for collecting the data, data preprocessing, analysis, developing predictive models, and collecting useful insights from the data for making data-driven decisions.
The skillset generally includes programming languages like Python, and R, good knowledge of Data Structures, Machine Learning algorithms, Mathematics, and some other software tools used in analysis and other processes.	The skillset generally focuses more on data collection, data preprocessing, analysis, data visualization, developing predictive models, Machine Learning, and getting useful insights from the data for data-driven decisions.
Work in collaboration with interdisciplinary teams, including other Data Scientists, software engineers, and domain experts.	Work in collaboration with other professionals, including other data scientists, other engineers, domain experts, and business people for providing data-driven decisions.

What is better – Machine Learning Engineer or Data Scientist?

Well, after knowing about what is Machine Learning Engineer, and what is Data Scientist, one more question that can come to mind is – what is better then? Is it Machine Learning Engineer, or Data Scientist? What should one choose to become?

Well, there can be various parameters when it comes to making this decision. For both roles, there would be some overlapping, but there are somewhat differences, which separate the Machine Learning Engineer, and Data Scientist.

Remember that Data Science is a bigger umbrella, and Machine learning comes under it. So, if you choose to learn Data Science and become a Data Scientist, then there are going to be a lot of things to learn, including Machine Learning.

So, you can go with what you find better. There are many things to consider, like what is the pay scale for both roles, what is the work going to look like, what are the future opportunities, etc.

We can say that Machine Learning engineers focus more on the engineering part of taking the models to production, while data scientists focus more on developing the right set of models, for particular business problems.

Both play very important roles in the teams, and they have the somewhat varied skillset. So, actually, it is not about who is better than the other, but it is about which role is important in which situation.

For example, if the project includes data collection, data preprocessing, data visualization, data analysis, developing predictive models, and getting useful insights for data-driven decisions, then Data scientists should be preferred for the project. On the other hand, when it comes to the design, development, and deployment of Machine learning models into production, then a machine learning engineer would be a better fit for the work.

So, you can perform some more research and decide what is better for you, since there are various parameters when it comes to making a decision, and many parameters are even volatile. So, real-time.

Conclusion

In this article, we have tried to look at some of the points of difference between a Machine Learning Engineer, and a Data Scientist. We did not dive into technical details, but we tried to keep things simple so that someone can easily understand the basic differences, and act accordingly.

I hope that you find this article useful. You can explore a lot about Machine Learning, Artificial Intelligence, and Data Science. There is a lot to learn, explore and implement in these fields. These are revolutionary fields and can have great opportunities.

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