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ML Engineer vs Data Scientist – Which is Better?

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  • Difficulty Level : Easy
  • Last Updated : 23 Nov, 2022
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In today’s modern world, thousands of data are generated daily; thus, it has become necessary to process it. To process it, we need powerful automated devices and to build those devices, professionals like Data Scientists and Machine Learning Engineers are in high demand. The debate goes on as to which profession is better. Let’s understand the difference between Data Scientists and Machine Learning Engineers.

ML Engineer vs Data Scientist


Data Scientists are analytical experts who analyze and manage a large amount of data using specialized technologies. This profession offers and is amazing satisfaction rating of 4.4 out of 5. Further, it is described as one of the most desirable professions in the 21st century. Machine Learning Engineers are those who focus on researching, building, and designing self-reliant artificial intelligence (AI) systems to automate predictive models. As per the survey, the demand for Machine Learning Engineers is expected to grow by 43 percent which is far more than the average.

Let’s talk about their work, importance, roles, and responsibilities one by one so that you can find out the better career domain for yourself Data Scientist or Machine Learning Engineer:

Data Scientist

What Does a Data Scientist Do?

From finding a solution for the best new diabetes treatment to identifying and thwarting national security threats, the ability to convert a set of data into actionable insights can have an intense impact. This is why private and government agencies are moving to hire data science professionals who can do it very quickly and efficiently. They gather, derive, and process the incoming data to bring out a valuable output.

There are several other related profiles also like data analysts and data engineers. One should have a good knowledge of statistical analysis, programming, and machine learning as well to become a data scientist. They also work on special areas like speech analytics, text, image and video processing, etc. 

Skills Required to Become a Data Scientist :

Given below are the top skills that are required to become a data scientist:

  • Programming: This is the first and very important skill any data scientist should have. Good knowledge of relevant languages like Python makes it better to excel in this career. Knowing programming languages helps professionals organize unstructured data sets.
  • Mathematics: Various mathematical concepts like Linear Algebra, Statistics, Probability, Calculus, etc. play a vital role in Data Science, hence you must be familiar with these concepts. To get more information about the basic statistics concepts of data science click here.
  • Knowledge of Analytical Tools: For extracting valuable information from an organized set of data, an understanding of relevant analytical tools is a necessary skill. Some of the tools which data scientists use are Hadoop, SAS, Hive, Pig, and R.
  • Data Extraction, Wrangling, and Visualization: Extract data from sources like MySQL and MongoDB and transform it to store in a particular format and structure for querying and analysis. Then load data to Data Warehouse to analyze it. Data Wrangling is cleaning the messy and complex data sets to ease access. There are tools like Tableau, and Power BI good for data visualization.
  • Model Deployment: The method to deploy a machine learning model in a live environment. It is a key step to gaining operational value from machine learning. Deploying them into production is an important step to allow businesses to start using them in practical ways.
  • Machine Learning: With machine learning, you can build predictive models. Machine learning algorithms are needed to predict anything that is going to happen in the future based on past data. From simple linear and logistic regression models to advanced ones like Random Forest, XGBoost, and CatBoost, all are the best models to handle data, and also give low error rates.

Roles and Responsibilities of a Data Scientist : 

  1. The very first role of a data scientist involves researching and developing statistical models for data analysis.
  2. Also, the responsibility includes understanding the needs of the customers and designing models or leading them toward solutions.
  3. Identifying new opportunities in the industry and thus designing models keeping that in mind that will help in the improvement process of the company.
  4. Use of databases and designs of projects that are used to optimize the solutions that are being faced while involved in a project. Also, the processing, cleansing, and checking of the integrity of data used for data analysis.

Machine Learning Engineer

 What Does a Machine Learning Engineer Do?

Those sophisticated programmers who develop and train machines are machine learning engineers. They automate predictive models by researching, building, and designing self-running software. They build artificial intelligence (AI) systems that hold huge data sets to generate and develop algorithms that are capable of making predictions. The system learns from experience and holds those results for future operations. 

Designing machine learning systems requires Machine Learning Engineers to assess, analyze, and organize data, execute tests, and optimize the learning process to help develop high-performance machine learning models.

Skills Required to Become a Machine Learning Engineer

Given below are the top skills to become a machine learning engineer:

  • Programming: Again, programming is considered to be the most important skill machine learning engineers should have. One also should have a good grip on computer concepts.
  • Mathematics: Mathematics is an important skill needed for becoming a machine learning engineer. Important concepts such as probability, linear algebra, multivariate calculus, statistics, and distributions like Poisson, normal, binomial, etc. should be evident.
  • Data Modeling and Evaluation – This includes understanding the structure of data and finding data patterns that will be suitable. Using the algorithm, evaluate the data that is suitable for it.
  • Advanced Signal Processing – The main use is to minimize noise and extract the best features of a given signal. Some concepts like Algorithms such as wavelets, shearlets, curvelets, contourlets, bandlets, and convex/greedy optimization theory.
  • NLP – abbreviated as Natural Language Processing Learn, creates devices and systems to understand, interpret and manipulate human language. Libraries and techniques of NLP are Word2vec, Sentiment analysis, Summarization, etc.
  • Neural Networks – They can be called a set of algorithms, loosely modeled after the brain of humans designed in a way to recognize patterns. They interpret data using a type of clustering or labeling raw input and machine perception.

To get more information about the skills required to become a machine learning engineer click here.

Roles and Responsibilities of a Machine Learning Engineer: 

  1. To analyze the data science technology and design them into machine learning models. Also, associated with data engineers to develop data and model pipelines.
  2. To design distributed systems, the application of data science and machine learning techniques is equally important.
  3. Writing production-level codes to improve the existing machine learning models to make that code suitable for production to getting involved in the code reviews and learning from them on what changes are to be made.
  4. Choice of appropriate datasets and the proper data representation methods, running machine learning tests and experimenting on them, performing statistical analysis, and fine-tuning using these test results.

Differences between Data Scientists and Machine Learning Engineers: 

Data Scientists  Machine Learning Engineers 
Data Scientists are analytical experts who analyze and manage a large amount of data Machine Learning Engineers are those who focus on researching, building, and designing self-reliant artificial intelligence (AI) systems to automate predictive models. 
Skills needed to build a career as Data Scientist: Statistics, Data analysis, and visualization, Machine learning, Data Wrangling, SQL/NoSQL, Programming skills, Math, Probability, and Coding Skills (Python, R, etc.) Skills needed to build a career as Machine Learning Engineer: Prototyping, Data Modeling, Programming skills (Python, SQL, Java, etc), statistics, Probability, 
Helps in developing data annotation strategiesHelps in controlling the version of models, experiments, and metadata
Data Scientists help in the development of custom tools in order to optimize the complete modeling workflowMachine Learning Engineer helps in the development of custom tools in order to optimize the complete deployment workflow
They work by visualizing and analyzing the data at various stages of Machine Learning lifecycleThey work by optimizing numerous models for memory, performance, throughput, and latency
Career Path: Data Engineer, Data Analyst, Data Scientist, Business Intelligence Analyst, Data Architect, etcCareer Path: Cloud Engineer, Machine Learning Engineer, AI Engineer, Human-centered AI systems designer, Computational linguist, etc
The average growth rate of Data Scientists: is 30.0% per yearThe average growth rate of Machine Learning Engineer: 42.8% per year


Skills like programming and good communication are required by both professionals. So switching from one domain to another won’t be too challenging. From this you can infer, both data science and machine learning are outstanding career options and there are great opportunities in both of them.

So, instead of debating on which one is a better profession in data science and machine learning, it will be beneficial to know that both of the professions are best in their way. Both of them are highly paid professionals. It depends on an individual’s interest in which domain one wants to work on. In both career opportunities, one needs to have wide knowledge, which leads to the best career decisions. Although both are different from each other but play an important role in the development of an organization.

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