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

Although both machine learning engineers and data scientists play important roles in the automation industry, they are new in the market. Because of this reason, some confusion is there among people regarding these roles. But by understanding these roles in detail, one can easily understand the actual difference and choose the right career path.

When we compare these two roles, we are virtually comparing scientists with engineers.As we all know, scientists are responsible for understanding the science of the whole process and its working, and engineers are responsible for creating something new.When we realize these two roles in this way, things become clear. However, you need to understand the difference between the two roles in detail before a career option for you.

First, you need to understand the difference between data science and machine learning in order to get the difference between these two roles.

Data Scientist Vs. Machine Learning Engineer

Who Is A Machine Learning Engineer?

Machine learning engineers gather knowledge of both data science and software engineering fields. They use data tools and programming skills to refine the gathered raw data as per requirement. A machine learning engineer is also responsible for feeding the data into models and scale the theoretical data science models to production level models that can handle a heavy amount of data. Machine learning engineers usually build algorithms to allow machines to think and program themselves.

Machine learning engineers also develop programs that enable machines to understand things without any help. Since they belong to the artificial intelligence field, their main objective is to build such systems that can function themselves. On the other hand, programmers are the professionals who build specific programs to perform specific tasks.

Who Is A Data Scientist?

A data scientist is a professional who collects data from different sources, analyses that, and provides useful insights from the data. Data scientists usually understand all details of the business and build programs to analyze that. Further, they perform several experiments that can help businesses to grow. They usually perform statistical analysis and research to build algorithms and prototypes for testing.

Data scientists use their expertise in science to solve complex problems in datasets. They use several skills like text, video, and image processing, speech analysis, and others to perform their tasks. Data scientists commonly have limited responsibility in the industries. So, there is a high demand for data scientist posts in the market. They play an important role in answering questions or solving problems by providing useful insights from the data.

Data Scientist Vs. Machine Learning Engineer

Aspect Data Scientist Machine Learning Engineer
Primary Focus Analyzing and interpreting complex data sets to extract insights and inform business decisions. Designing, developing, and implementing machine learning models and algorithms to solve specific problems.
Key Skills Statistical analysis, data wrangling, machine learning, data visualization, domain expertise. Strong programming skills (Python, Java, etc.), machine learning frameworks (TensorFlow, PyTorch), software engineering, algorithm design.
Data Handling Deals with a broad range of data sources and types, including structured and unstructured data. Focused on structured data and often requires preprocessing and feature engineering to prepare data for machine learning models.
Problem Solving Addresses a wide range of business problems using statistical and machine learning techniques. Concentrates on developing solutions to specific problems using machine learning algorithms.
Responsibilities Develops models, conducts exploratory data analysis, communicates findings, and collaborates with cross-functional teams. Implements machine learning algorithms, optimizes models for performance, deploys models to production, and maintains the ML pipeline.
End Goal Providing actionable insights and recommendations based on data analysis to support business decisions. Building and deploying machine learning models to solve specific problems or automate processes.
Deployment Focuses on the analysis phase and may not always be involved in deploying models to production. Has a strong emphasis on deploying models to production, optimizing for scale, and ensuring integration with existing systems.
Tools Uses tools like R, Python, SQL, and various statistical analysis tools. Works with programming languages like Python or Java, and frameworks like TensorFlow, PyTorch, scikit-learn.
Background Typically has a background in statistics, mathematics, or a related field, with knowledge of business domains. Often has a background in computer science, engineering, or a related field, with a focus on software development and machine learning.
Workflow Involves understanding business problems, collecting and analyzing data, developing models, and communicating results. Involves understanding the problem, designing and implementing machine learning models, deploying models to production, and maintaining the ML infrastructure.

Role Requirements:

Both machine learning engineers and data scientists have some similarities in their specific roles. Data scientists perform a statistical analysis of the data and decide the appropriate machine learning process. Then they prepare algorithms and prototype according to that to test. After that, machine learning engineers use that model and make it suitable for the production environment.

As mentioned earlier, machine learning engineers don’t need to understand the science and working of the models as data scientists do. So, the requirements for these two roles are different. Here are the requirements for the data scientist and the machine learning engineer job roles.

Requirements for Machine Learning Engineers:

The candidates should have a master’s degree in computer science to apply for the machine learning engineer role. They should also know the machine learning algorithms and how to implement them in order to be eligible for this role.Further, the candidates should be familiar with some programming languages like Java, Python, C, C++, Julia, R, Scala, and JavaScript.

The following are the basic requirements for the machine learning engineer role.

  • Experience in Java, R, Python, Scala, and other programming languages is required.
  • The candidates should have a Ph.D. or master’s degree in mathematics, computer science, or statistics.
  • Working experience in messaging tools like ZeroMQ, Kafka, and RabbitMQ.
  • In-depth knowledge of mathematics and statistics because this job is to train machines to think and communicate.
  • Knowledge and experience in machine learning.
  • Knowledge of engineering and strong analytical skills.
  • Experience in MATLAB
  • Working experience with huge datasets

Requirements for Data Scientists:              

High educational qualification required to apply for the data scientist role like machine learning engineer. To apply for this role, you should have a Ph.D. or master’s degree in computer science, statistics, or mathematics.

The requirements for the data scientist job role are as follows:

  • Working experience in Java, Python, and SQL.
  • Strong knowledge of statistical concepts and processes.
  • Strong mathematical and analytical skills.
  • Working experience with machine learning practices like clustering, neural networks, decision tree learning, etc.
  • Five to seven years of experience in developing statistical models and handling large datasets.
  • Experience in data mining techniques such as generalized linear models, random forests, social network analysis, etc.
  • Working experience with data and computing tools such as MySQL, Hive, Hadoop, Gurobi, Spark, etc.
  • Experience in analyzing the data from third-party data providers like Coremetrics, Facebook Insights, Google Analytics, Hexagon, Site Catalyst, etc.
  • Experience in visualizing and demonstrating data using tools like D3, Business Objects, Periscope, etc.


Since these two roles have different requirements and perform different tasks in the industry, the responsibilities of these roles are also different. Here are the responsibilities of these roles.

Responsibilities of Machine Learning Engineers:

Mainly, machine learning engineers are responsible for developing algorithms for statistical models and maintain machine learning solutions. However, the responsibilities of the machine learning engineers can vary depending on the projects.

Commonly the machine learning engineers are responsible for the following tasks.

  • Build machine learning models and algorithms and implement those.
  • Develop data and model pipelines by cooperating with data engineers.
  • Be accountable for researching, designing, monitoring, experimenting, deploying, maintaining, and developing algorithms and machine learning models.
  • Design distributed systems by using data science and machine learning practices.
  • Develop a production-level code and review the code.
  • Address complex processes to appropriate stakeholders.
  • Obtain useful insights by analyzing complex data sets.
  • Perform research and implement advanced techniques to improve existing machine learning models.
  • Develop prototypes to help in future research.
  • Build machine learning features and deliver those by working with other research teams.

Responsibilities of Data Scientists:

Data scientists are responsible for storing and refining a large amount of data. They usually research data sets to determine useful insights and develop predictive models. They know both statistics and programming skills to perform their tasks efficiently.

The responsibilities of a data scientist are as follows:

  • Build specific data models and algorithms.
  • Perform research and build statistical models for analysis.
  • Explain statistical concepts and results to business leaders
  • Improve the developments by using proper databases and project designs.
  • Develop tools and processes to analyze and monitor data accuracy and performance.
  • Perform A/B testing and test the quality of the model.
  • Improve customer experience, revenue, ad targeting, etc., by using predictive modeling.
  • Understand the needs of the business and plan solutions by communicating with other departments.


Salary of A Machine Learning Engineer:

Machine learning engineers usually develop programs for machines to automate them. They can perform various tasks in the industry, and their salary varies according to their expertise, location, and responsibilities. The salary of machine learning engineers varies from 5 lakh to 20 lakhs per annum in India.

Salary of A Data Scientist:

Data scientists also get a salary depending on their skills, location, and roles. They can earn from 5 lakh to 17 lakhs per annum according to their experience.

Bottom Line:

Both data scientist and machine learning engineer roles are excellent options to select. No matter which role you select, you will work in the business and technology field and can have a bright career ahead. It completely depends on your interest in which role you want to see yourself. Both roles need a master’s degree to hire the candidates, but some companies also prefer to hire candidates with appropriate skills and experience.

Data scientists usually deal with large chunks of data, while machine learning engineers do coding and develop machine learning models. The salary of each role varies according to the role they play in the business.So, you can achieve success by improving your skills and experience, whether you become a machine learning engineer or a data scientist.

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