Data scientist vs Machine learning engineer: role, skills, and major differences

By HH Team | Views: 258

The two most popular tech roles in 2021 are data scientists and machine learning engineers. Depending on the organization that you’re employed with these roles can either interchange, overlap or remain distinct. However, to apply for jobs in these positions, you need to acquire certain skill sets and understand the major differences between the two. 

Overlapping between the two-tech role is quite possible, so let’s understand in detail the special skills and differences between the two.  

In general, the data scientists work on the modelling side while machine learning engineers focus on the deployment of that model. Data Scientists' whole sole attention is on the ins and outs of the algorithm, while machine learning engineers work to develop a model that interacts with its users. 


Major Skills required to become a data scientist


Data Scientists are expected to be well versed with Python as they work almost every day on the popular programming language. If not working on Python they work on R. The purpose is the same for both.  The aim is to extract data, explore it, process it, feature engineer, model build, and communicate results. 

Jupyter Notebook

Data Scientists uses Jupyter Notebook a lot as it provides a common central place to code, write texts and view outputs. This tool is a must-have for data scientists across the globe. The notebook allows users to add extensions to make coding easier. 


Another very important structured query language for data scientists is SQL. Data Scientists need to use SQL for processes like querying the first data and for creating new features.

If the above-mentioned skills are mastered properly, it can make you a great data scientist. 


Important Skills to become a Machine Learning Engineer


Machine learning engineers need to be highly trained in Python. Machine Learning Engineer’s prime focus is on object-oriented programming whereas data scientists’ job is just to build models and focus on analytics and statistics. 


The GitHub platform is vital for machine learning engineers as they use the tool to store code repositories. This code management tool is also used to make code changes and pull requests. This platform can be used by both data scientists and machine learning engineers as an important skill. However, machine learning engineers work more on GitHub.

Deployment Tools

In this skill, both data scientists and machine learning engineer differ a lot. There are various deployment tools available like Google Cloud, Azure, AWS, Docker, Airflow etc. The prime job of a machine learning engineer is to deploy data science models.

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