Machine Learning Engineer vs Data Scientist (Is Data Science Over?)

Machine Learning Engineer vs Data Scientist  (Is Data Science Over?)

Machine Learning Engineer vs Data Scientist (Is Data Science Over?) vs Data Analyst vs Research Scientist vs Applied Scientist vs…

Man, this topic has been in the back of my mind for a long time. But because there are so many things to potentially cover, I couldn’t get myself to finish this daunting task. But, stuck in my room due to the shelter-in-place order and running out of things to waste time with, I finally decided to finish it. As its popularity has exploded since 2013, the data science industry has been wildly evolving yet slowly converging into more specific roles. Inevitably, this caused confusions and inconsistent job functions during its growth. For example, there are seemingly many different titles with the exact same roles or same titles with different roles. The list goes on. Even for me, recruiters have reached out to me for positions like data scientist, machine learning (ML) specialist, data engineer, and more. Clearly, the industry is confused. One of many reasons for such a high variance is that companies have very different needs and uses of data science. Regardless of the reason, it appears that the field of data science is branching and merging into these top few categories: Analytics, Software Engineering, Data Engineering, and Research. No matter what the similar titles say, they usually fall into these categories. This specialization is most true in larger tech companies that can afford it.

technology artificial-intelligence software-engineering machine-learning data-science

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