Key takeaways
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When comparing machine learning engineer versus data scientist jobs, you’ll find differences in how each professional works with data and some of the skills they use.
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When comparing machine learning engineer versus data scientist salary figures, machine learning engineers slightly outearn data scientists with median salaries of $165,000 and $158,000 [1, 2].
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Data scientist versus machine learning engineer roles share some overlap, as they often work together in supportive roles, and both require programming proficiency, statistical knowledge, and communication skills.
The similarities between machine learning engineer versus data scientist tasks make it possible for a professional in one role to transition to a career in the other. Learn more about the similarities and differences between machine learning engineers and data scientists below, and discover more about what it takes to succeed in these positions.
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Data scientist vs. machine learning engineer roles
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.
What do machine learning engineers do?
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 that 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 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:
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Build machine learning algorithms for data analysis and predictive models to solve problems.
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Perform testing on machine learning software and correct any bugs that may arise to ensure the program is functioning as designed.
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Consult with management on machine learning processes and provide documentation.
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Find opportunities for improvement in different systems and technologies that utilize machine learning.
What do data scientists do?
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:
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Build statistical and machine learning models to analyze data and identify meaningful patterns and trends.
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Collect data from various sources and clean it to be consistent, accurate, and usable for processing.
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Communicate findings to stakeholders, build visualizations to present the data, and make recommendations based on the findings.
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Perform research and continuously learn about the latest technologies and techniques, such as reinforcement learning and deep learning.
Can a machine learning engineer be a data scientist?
Yes, because machine learning engineers and data scientists have similar skills and share some similar responsibilities in their roles, a machine learning engineer is well-equipped to transition to a data science career. Similarly, data scientists can transition to machine learning engineering roles. To make the switch, taking online courses, such as those available on Coursera, can help you fill skill gaps in preparation for your new position.
Education requirements for a machine learning engineer vs. a data scientist
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 a Google Professional Machine Learning Engineer Certification.
Your education requirements to work as a data scientist are similar to those 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 boot camps are another option to learn the necessary data science skills over a much shorter period of time.
Skill requirements of machine learning engineers and data scientists
Machine learning engineers and data scientists have some overlap when it comes to both technical and workplace skill requirements.
Machine learning engineering skills
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Programming proficiency in languages such as Python, R, Java, and C++
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Knowledge of math concepts, including linear algebra, probability, and statistics
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Knowledge of cloud-based programs that support machine learning and artificial intelligence
Data science skills
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Programming proficiency in programming languages, including Python, R, SQL, and Java
Machine learning engineer vs. data scientist salary and job outlook
According to Glassdoor, the median total pay for machine learning engineers in the US is $165,000 per year, while data scientists earn $158,000 per year [1, 2]. These figures include base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation. 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 a median total salary of $193,000, while data scientists in New York earn $172,000 [3, 4].
The machine learning market is growing, with demand increasing for machine learning professionals. The global market is projected to reach $684.4 billion by 2033, up from its estimated 2026 market valuation of $135.8 billion [5]. Data science also offers a strong outlook, with the demand for data scientists in the US projected to grow 35 percent from 2025 to 2035, according to the US Bureau of Labor Statistics [6]. Now may be a great time to pursue a career in one of these growing fields.
Explore our free machine learning and data science resources
Join Career Chat on LinkedIn to stay current with popular skills, tools, certifications, and more. Then, continue your learning journey with data science and machine learning with our other free digital resources:
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Read our Career Chat issue:6 Machine Learning Certificates + How to Choose the Right One for You
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