Demystifying data science roles. Toward data science. Yorgos Askalidis. Aug 6, 2019

“The “data scientist” has cemented its legacy in pop culture as the vague buzzword describing anyone who can access and make any sense of the data that power so many of our experiences today. Data scientists of all types are in, seemingly, hot demand. A search on LinkedIn for “Data Scientist” job postings will return 28 thousand postings for the United States alone. But searches for “Data Analyst”, “Machine Learning Engineer”, even “Data Engineer” and “Data Visualization” will return tens of thousands of postings each.

How can you make sense of the differences between these positions, especially as you’re entering the data science job market for the first time?

In my three years at Spotify I had a change in title, from “Data Analyst” to “Data Scientist”, without my role changing. And from there, a change in my role, from product to finance, without my title changing. New hybrid positions have also been created spanning data science, data engineering, data visualization.

Each of these roles has its own set of stakeholders and requirements of subject-matter expertise.

In this post I’m offering my thoughts and experience on some of the main types of data science jobs that I have come across in the market so you can identify positions best suited to you and interview prep accordingly”.

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