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If you are interested in the field of data, then you have probably come across the terms data scientist and machine learning engineer mentioned once or twice. Before you make a decision about which of these two career paths would be better for you, it is important to know what each one has to offer.

Both areas work with data in some way, and that is one of the main reasons why oftentimes there is some confusion regarding the difference between data science and machine learning. However, the objective of data science is to extract information and insight from data, whereas machine learning aims to develop the techniques that data scientists can use when working with data. To understand this distinction better, we will explore the contrast between the two by focusing on the responsibilities that professionals have, the career paths they can follow, the difference in salary, and the necessary skills you are expected to have developed for each role.

Data Scientist vs. Machine Learning Engineer: Job Responsibilities

To establish the difference between machine learning and data science, we must overlook the fact that they both work with data and focus on what they do with it. So, let’s have a look at the job responsibilities both data scientists and machine learning engineers have.

The main responsibility of data scientists is to gather, process, and extract valuable insight from the data of the company, and use it to make better business decisions. As a data scientist, you would also be responsible for:

  • Finding new ways to gather sources of data and evaluating their accuracy.
  • Working with stakeholders to determine how the company data can be used for the benefit of the business.
  • Using predictive models to improve customer experience and ad targeting.
  • Analyzing databases and using the results to develop better marketing strategies and other business processes.

On the other hand, as a machine learning engineer, your main responsibility would be to design, develop, and implement machine learning systems, algorithms, and tools that can be used by data scientists to better understand data information.

Other responsibilities include:

  • Running machine learning tests in order to improve ML models based on the results.
  • Choosing the appropriate data sets and algorithms to perform statistical analysis and assessing the data quality.
  • Noticing the differences in data distribution that affect the performance of ML algorithms.
  • Developing machine learning applications in agreement with the customer’s requirements and expanding the machine learning libraries of the company.

Although data scientists and machine learning engineers are oftentimes involved in the same projects, they take quite different approaches when dealing with data. While data scientists work towards researching and analyzing the data they gather, the machine learning engineers will be helping build the necessary software systems and algorithms that are then used by other professionals of data-related fields.

Data Scientist vs. Machine Learning Engineer: Career Path


Each individual goes through their own unique career path, however, we can make some broad generalizations about what you should expect on your road towards becoming a data scientist or a machine learning engineer.

According to the U.S. Bureau of Labor Statistics, to pursue a career as a data scientist, a machine learning engineer, or some type of computer and information research scientist, you should first have a bachelor’s and master’s degree in data science, computer science, or another related field, such as computer engineering, information technology, math, and statistics. However, keep in mind that after finishing your education, oftentimes you can’t jump straight into the positions of a data scientist or machine learning engineer, but instead, you start with entry-level positions.

In the case of data science, you can begin by working as a junior data scientist or a data analyst. Then, with time having earned more knowledge and experience, maybe even pursuing a doctorate degree, you will be qualified to be promoted to not only a data scientist but for a senior-level job position and/or other executive roles.

Similarly, when pursuing machine learning, you can begin working as a software engineer, programmer or developer, computer engineer, or some other similar position, and make your way up from there.

Data Scientist vs. Machine Learning Engineer: Salary

When discussing the professions of a data scientist and machine learning engineer, it is important we also consider the average salary each one offers.

The average salary for data scientists in the United States is $119,935 per year. The highest-paying cities in the U.S. are:

  • San Francisco, CA with $156,441 per year
  • Santa Clara, CA with $156,284 per year
  • New York, NY with $140,106 per year
  • Austin, TX with $131,078 per year
  • San Diego, CA with $124,679 per year


Alternatively, for those who pursue a career as a machine learning engineer, the average salary in the U.S. for this profession is $150,617 per year. The cities where salary is the highest include:

  • Cupertino, CA with $202,400 per year
  • San Francisco, CA with $199,321 per year
  • Santa Clara, CA with $181,131 per year
  • Austin, TX with $174,726 per year
  • New York, NY with $165,798 per year

You should remember that all these salaries can vary depending on the company where you work, the work you do, your skills level and years of experience in the job.

Data Scientist vs. Machine Learning Engineer: Skills


Besides the educational knowledge, all employers value certain technical and non-technical skills in their workers. This way we can make another comparison between the two professions based on the skills they need to be successful in their fields.

When it comes to skills, both data scientists and machine learning engineers need to develop similar hard and soft skills. They should be proficient in Python, R, and SQL since they are some of the most popular programming languages, beginner-friendly, and are considered essential for a functional data science foundation. Then, professionals from both fields should also be knowledgeable in a number of other platforms, such as Spark, Hadoop, and Apache Kafka.

In addition to the previously mentioned skills, some soft skills are necessary as well. Like with many other professions, data scientists and ML engineers also need to have good communication skills, be curious and creative with their approaches, as well as be team players.

Regardless of all the differences, we have listed between these two professions, they both encompass important responsibilities and skills, as well as offer amazing opportunities. Therefore, whichever you end up choosing will be a great pick.



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