Dec 14 2022

Are you fit to become a Data Scientist or a Machine Learning Engineer?

Mikael John

Career Advice

Everyone probably knows that Data Science has two main branches: Data Scientist and Machine Learning Engineer (ML Engineer). However, very few people can distinguish the difference between these two industries, and if you and your colleague are working in the same company, what jobs will they do?


Hopefully, through this article, you will have a better view of Data Scientist and ML Engineer, and can easily choose the direction for themselves.


Start, starting point and background


In terms of knowledge, both Data Scientist and ML Engineer need knowledge of Machine Learning, AI and even Deep Learning. However, when we go deeper, we can see a clear difference as follows


Data Scientist

Data Scientists often come from many different backgrounds such as finance, marketing, banking, chemical engineering... An interesting fact is that in the companies I have worked with, 69.96% of Data Scientists are NOT from IT.


Data Scientists also don't need to excel in programming. For them, programming or coding is just a tool to analyze, process and model data.


Companies often require Data Scientists to have a postgraduate degree such as a Master (master) or PhD (PhD). The original Data Scientist team that I work with has 6, 2 of them are PhDs, and the other 4 are also Masters! 


Depending on the company's needs and technology, Data Scientist uses R, Matlab or SPSS, not necessarily Python. In companies that use Microsoft Window Server, the data model in Python will be difficult to integrate into the Window Application, instead Data Scientist builds the data model using Matlab.


In terms of knowledge, in addition to Domain Knowledge (specialized knowledge) is required, Data Scientists also need Research skills to refer to Papers (scientific documents) and apply to their work.


Machine Learning Engineer

ML Engineer comes from 96.69% from IT, computer science and information technology

Because they are all Engineers, ML Engineers can be considered a branch of Software Engineers. In some big companies like Apple or Google, this position is called “Software Engineer, Machine Learning”.


ML Engineer is required to have strong programming knowledge, the main language used is Python, Scala or Java. But unlike Back Engineer, ML Engineer is completely focused on the data field.


In addition to knowledge of Data Science or ML/AI, they also need to know about APIs and DevOps because they are also the ones who make sure the data model or API always runs stably and makes the most accurate predictions.


Specifics of work and profession


Briefly talking about the characteristics to identify two "pokemon", confusing these two professions is enough. So, is there any difference between working and working? And how is it different?


In each company, the definition of Data Scientist and Machine Learning is different, the job content will also be different. But in summary, the main features can be mentioned are.


Data Scientist

Usually related to Business and focuses on solving business problems. For example, predicting customer consumption, predicting fraud, studying data models to predict cancer recurrence, etc.


Data Scientist's work will be research-oriented. They try different types of data and different methods to solve problems using Machine Learning.


Data Scientists often meet with Stakeholders (customers) to present the insights / findings that they find. Every day, a Data Scientist will spend about 40% of time on preparing slides and presentations.


In addition, Data Scientists are also data modelers. However, this data model only stops at the Prototype level (testing) to prove their hypothesis correct and solve the problem.


Machine Learning Engineer

The main job of ML Engineer is to build a pipeline system to bring the data model built by Data Scientist into a product, more specifically, an API. To add a little more, in some companies that do not have the position of ML Engineer, this job is taken by a Data Engineer.


ML Engineer is also responsible for that API system. They need to make sure the system always runs correctly and has the ability to scale to meet user needs all the time.


In some companies, systems such as image recognition, speech recognition, recommendation systems are assumed and built by ML Engineers. In general, the job is more about Computing than solving business problems.


Because he is also a Software Engineer, ML Engineer still follows the software development process.



In short, here is a comparison table of Data Scientist and Machine Learning Engineer.



Data Scientist

ML Engineer


Economics, finance, marketing..

Computer Science / IT


Master, PhD



Research, R&D, Business Solving

Computing, Software Engineer

Tech skills

Data Mining, Machine Learning, AI, Visualisation, Python, R, SPSS, Matlab,..

Algorithms, Data Structure, Machine Learning, AI, Docker, CI/CD, Deployment, Python, Java, Scala..


Presentation, communication, story telling

Team work, individual

Note that the title in some companies may be different or ambiguous between these two jobs. In this case, you should carefully read the job content to understand clearly before applying for that position.


Source Data Guy Story

Tags: become a Data Scientist or a Machine Learning Engineer