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Try asking someone working in the data field ‘What’s the difference between a Machine Learning Engineer, a Data Scientist, and a Data Engineer?’
Compare the answers and I’m sure you’ll notice the following:
There is no clear answer
All roles overlap one another
Some responsibilities can be handled by them all
And most importantly you did not understand 😆 so let’s make sure you get an answer by the end of this blog.
The blog is composed of four sections:
Section 1 focuses on the role of a Data Scientist
Section 2 focuses on the role of a Machine Learning Engineer
Section 3 focuses on the role of a Data Engineer
Section 4 explains the collaboration between the three roles
Let’s dive into each section.
1. Who’s a Data Scientist?
A Data Scientist’s primary job is the analysis of the data and drawing meaningful insights from it. This role focuses on the data related tasks: extracting, cleaning, transforming, and visualizing data to derive valuable insights from it. He can also use this data to build predictive models for testing purposes.
Another responsibility of a Data Scientist is data storytelling where he communicates insights through interactive dashboards in the context of a story using business intelligence tools.
2. Who’s a Machine Learning Engineer?
A Machine Learning Engineer’s primary role is to recognize hidden patterns to teach machines how to perform on unseen data. He takes the models prepared by the data scientists and deploys them to production.
This role focuses on the modeling related tasks: training, testing, comparing and fine-tuning models to deploy them into production at scale.
In addition, the Machine Learning Engineer will be responsible for monitoring these models once in production, and retraining them when necessary to make sure they are performing as expected.
3. Who’s a Data Engineer?
A Data Engineer’s primary job is to prepare data for analytical or operational uses. They are typically responsible for building data pipelines to bring together information from different source systems.
Data engineers often work as part of an analytics team alongside data scientists. The engineers provide data in usable formats to the data scientists and Machine Learning Engineers who run queries and algorithms against the information for predictive analytics, machine learning and data mining applications.
They also deliver aggregated data to business executives, analysts, and other end users so they can analyze it and apply the results to improving business operations.
4. Where does the confusion come from?
Although each role has its own title and responsibilities, it’s better to see the big picture where Data Scientists, Machine Learning Engineers, and Data Engineers work hand-in-hand on a project.
Some tasks can be done by a Data Scientist and a Data Engineer, others are common between Data Scientist and Machine Learning Engineer, but they all serve the same purpose and that’s what matters.
At the end of the day, you’ll choose one role and be in direct contact with the other two. Along the way you’ll be exchanging knowledge, contributing in one phase and learning in the other.