Machine Learning Engineer vs. Data Scientist: What’s the Difference?

Written by Coursera Staff • Updated on

Learn about the differences between machine learning engineers vs. data scientists, from education and skill requirements to job responsibilities.

[Featured Image] A machine learning engineer and data scientist meet in the office to discuss a project.

The machine learning market is growing, with demand increasing for machine learning professionals. The global market is projected to reach $170.2 billion by 2030, up from its current market valuation of $56.75 billion [1]. Data science also offers a strong outlook, with the demand for data scientists in the US projected to grow 35 percent from 2022 to 2032, according to the US Bureau of Labor Statistics [2]. Now is a great time to pursue a career in one of these growing fields. First, learn more about the careers of machine learning engineers and data scientists, including what they do, how to become one, what skills you will need, and how much you can earn in these roles.

Machine learning engineer vs. data scientist

Although data scientists and machine learning engineers work with data, how this occurs differs between the two positions. In some cases, machine learning engineers and data scientists may work together in supportive roles.

Machine learning engineer

Machine learning engineers build programs such as software applications, predictive models, and algorithms that enable computers and systems to find insights on their own. Within the data, machine learning engineers hope to find patterns or trends and learn to make accurate predictions. Ultimately, the programs machine learning engineers build learn independently after being taught how to do so. By teaching computers how to learn similarly to humans, the result is programs capable of sorting through significant amounts of data more efficiently and more effectively.

Machine learning has applications in various industries, including health care, transportation, manufacturing, finance, etc. Machine learning engineers are critical in developing the machine learning technologies in these fields, helping businesses solve problems and make important decisions. As a machine learning engineer, you will perform several tasks relating to artificial intelligence and machine learning, often working alongside software engineers, data scientists, and deep learning engineers. Here are some of the typical job responsibilities of a machine learning engineer:

  • Build machine learning algorithms for data analysis and predictive models to solve problems.

  • Perform testing on machine learning software and correct any bugs that may arise to ensure the program is functioning as designed.

  • Consult with management on machine learning processes and provide documentation.

  • Find opportunities for improvement in different systems and technologies that utilize machine learning. 

Data scientist

Data scientists help their organizations utilize data to make better decisions. This process involves collecting and cleaning data and creating statistical models that analyze the data. From there, data scientists look for any insights the data provides through trends and patterns. These insights enable organizations to implement data-driven decisions that help with areas such as identifying growth opportunities or solving problems. Data scientists sometimes will even use artificial intelligence and machine learning to analyze data.

It’s important for data scientists to have a strong understanding of their business, or domain, as well as the data, since they work alongside decision-makers, providing recommendations and guidance based on what the data suggests. This makes effective communication, aided by data visualizations, critical as others may not have the same level of technical knowledge but still need to understand the information. 

Data scientists work in various industries, including manufacturing, health care, government, retail, and energy. Practically all industries can benefit from implementing data science strategies. Here’s a look at some of the common job responsibilities of a data scientist:

  • Build statistical and machine learning models to analyze data and identify meaningful patterns and trends.

  • Collect data from various sources and clean it to be consistent, accurate, and usable for processing.

  • Communicate findings to stakeholders, build visualizations to present the data, and make recommendations based on the findings. 

  • Perform research and continuously learn about the latest technologies and techniques, such as reinforcement learning and deep learning.

Education requirements

For a career in machine learning engineering, you typically need at least a bachelor’s degree in an area such as computer science, information technology, software engineering, math, or statistics. You can also make yourself more competitive as an applicant for a machine learning role by completing a certification such as an AWS Certified Machine Learning - Specialty or a Google Professional Machine Learning Engineer Certification.

Your education requirements to work as a data scientist are similar to that of a machine learning engineer, with most employers expecting you to have at least a bachelor’s degree in computer science, data science, data analytics, or a related field. Many data scientists go on to earn a master’s degree as well. However, a degree isn’t always a requirement, and bootcamps are another option to learn the necessary data science skills over a much shorter period of time. 

Skill requirements

Machine learning engineers and data scientists have some overlap when it comes to both technical and workplace skill requirements.

Machine learning engineering skills

  • Programming proficiency in languages such as Python, R, Java, and C++

  • Neural networks and machine learning algorithms

  • Knowledge of math concepts, including linear algebra and probability and statistics

  • Data modeling

  • Knowledge of cloud-based programs that support machine learning and artificial intelligence

  • Communication and problem-solving

Data science skills

  • Programming proficiency in programming languages, including Python, R, SQL, and Java

  • Ability to build statistical and machine learning models

  • Data visualization tools such as Tableau 

  • Knowledge of probability and statistics

  • Data mining

  • Database management

  • Communication and critical thinking

Salaries of machine learning engineers and data scientists

The average salary for machine learning engineers is $125,558 [3], while data scientists earn $129,189 on average [4]. However, your earning potential can vary depending on a number of factors, such as your education level, how much experience you have, the industry you work in, and your job location. For example, data scientists in California earn an average yearly salary of $147,390, while data scientists in Florida earn $100,780 [5].

Getting started with Coursera

On Coursera, you can find highly-rated courses to help you learn more about machine learning and data science. Machine Learning Specialization from the University of Washington is an opportunity to learn about machine learning algorithms and how to analyze large, complex data sets.

If you want to learn more about data science concepts, Applied Data Science With Python Specialization from the University of Michigan will help you learn how to apply Python programming skills for data visualization, machine learning, and text analysis.

Article sources


Statista. “Machine Learning - United States,”” Accessed December 19, 2023.

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