Data Scientist, Machine Learning Engineer & Data Analyst: What’s the difference?

Introducing some of the popular roles in Data, and what their work involves with examples using cats 😺

My first job title in the tech was Business Intelligence Analyst , which is the predecessor to today’sData Analyst. It was 2010s, Big Data was all the rage, and everyone is talking about using MapReduce and Hadoop.

Since then, I have worked a variety of roles in Data, including Data Scientist , Applied Scientist , and Machine Learning Engineer. I have also mentored many students who made career changes into Data. One of the common questions they asked: what are the differences between DA (Data Analyst), DS (Data Scientist) and MLE (Machine Learning Engineer)?

In this post I’d like to explain what the different titles involve in terms of:

• Skills required
• Project & work examples
• Salary range & Number of openings

Data Job Categories

Jobs in Data can be divided in 5 main groups:

• Data Analyst : almost no machine learning (ML)
• Data Scientist / Applied Scientist:some ML, maybe some deep learning (DL)
• Machine Learning Engineer: a lot of ML, more…