ML Engineer VS. Data Scientist: What’s the Difference?

ML Engineer & Data Scientist

While there are many similarities between machine learning engineers and data scientists, their paths have diverged long ago enough to paint a real difference. Whereas before, the prevailing mentality was “we need to hire some data scientists”, now businesses are starting to see the need to hire more specialized functions. One way to study the relationship between these two characters is to depict the passing of the baton. Data scientists have the background and skills to perform statistical analysis to shape ML planning strategies. A data scientist will also build an algorithm, which is fed into an ML model. In this post, we’ll dive into the key differences (and similarities) between a machine learning engineer and a data scientist, and how the two roles fit together in the wider data science ecosystem.

Becoming A ML Engineer

Machine learning engineers typically have a master’s degree in computer science or a related form of data engineer training. However, education is only the foundation, not the guarantee, of a successful career.

Potential machine learning engineers should understand machine learning algorithms, have experience in software engineering and various programming languages, and have a solid understanding of mathematics and experience in data analysis.

It is also beneficial to have extensive experience dealing with big data.

With these skills in hand, your chances of working professionally as an ML engineer increase.

When it comes to data science vs machine learning, it is always important to acknowledge that ML exists under the umbrella of data science. While both types of professionals can work with machine learning data models, the training of an ML engineer will qualify him to delve into the specific training, development, and implementation of ML capabilities.

ML Engineer VS. Data Scientist

ML Engineer VS. Data Scientist

Machine Learning Engineers and Data Scientists can certainly work together harmoniously and have some overlap in skills and experience.

But, at its core, when it comes to machine learning engineers and data scientists, the names of the roles make a big difference in articulating the basic difference. Machine learning engineers go further than data scientists in the same project or company. Simply put, a data scientist will analyze data and gather insights from it.

A machine learning engineer will focus on writing code and deploying machine learning products.

Of course, machine learning engineers and data scientists are just the beginning of the nuances that exist in the relatively new data-driven discipline.

When it comes to careers in data, specializations and areas of focus are constantly changing and growing.

Key Functions 

While both machine learning engineers and data scientists are hands-on roles, their day-to-day jobs look very different.

Data scientists are often considered builders. They are responsible for analyzing and understanding specific business problems, feature engineering, developing, selecting, and tuning models, and then generating insights to present to stakeholders.

ML engineers, on the other hand, are primarily focused on taking these models and helping to scale them into production while ensuring compliance with business SLAs. They are involved in maintenance and monitoring, often not in creation, but in integrating models into business workflows. Especially in an enterprise environment, machine learning engineers are responsible for implementing risk mitigation strategies, ensuring that models perform optimally while striving to achieve defined policies.

If data science were a new housing development, data scientists would be responsible for creating custom blueprints and floor plans, which were then sold to developers before production, similar to how data scientists “sell” their model ideas to leadership. Machine learning engineers will be responsible for putting homes on the market, handling property maintenance, and making sure all residents are satisfied.

Skills Required 

The two roles have different functions within an organization, but they generally have many of the same skills and use the same technologies. ML engineers and data scientists are generally proficient in Python, have a background in mathematics and statistics, and are experienced in machine learning and predictive modeling.

However, data scientists often need to be more creative in their day-to-day work, as their goal is to use data to tell stories. They are often the people who are in direct contact with stakeholders, so they need to know how to generate insights and come up with effective solutions to pressing business problems.

Conversely, ML engineers tend to have more basic software engineering skills, and they are at the crossroads of data science and IT. They have a stronger foundation in data structures, algorithms, and creating deliverable software, and they almost always have a background in advanced computer science.

In terms of coding, as mentioned earlier, both roles are primarily in Python. ML engineers typically write low-level code to tweak and optimize default implementations, and they are also proficient in productive programming languages ​​such as C++, Java, and Scala. Data scientists write advanced code in Python or R, and they often use BI tools for data analysis and visualization. While ML engineers’ jobs revolve around machine learning, machine learning is just one tool in the tool belt of data scientists who may rely on data analysis and statistics for months.

Data scientists interested in transitioning to machine learning engineers should focus on one thing to improve their skills. By increasing their knowledge of data infrastructure management and algorithms, while working more closely with ML engineers to make models usable, valid, and valuable to users, data scientists will make themselves more attractive candidates.

Average Salary for Data Scientists and ML Engineers

Probably due to their higher technical skills, when comparing the salaries of ML engineers and data scientists, the average salary of machine learning engineers is generally higher. In addition, supply and demand will also have an impact on candidates with the required skills for ML engineers, with more organizations focusing on operating models.

In fact, the average annual income of a data scientist in the United States is $109,802, while the average annual income of an ML engineer is $132,651. As demand for both career paths increases, the average salary for both positions will likely continue to increase as well


While some companies prefer a well-rounded scientist who has both data science and machine learning (operations) capabilities, many companies prefer an expert in one field because they will separate the two roles within the team. It is important for one person to do everything from start to finish, so having two designated people, one focused on model building and the other on model deployment, is often a more efficient approach.

Tina Jones