What has been happening to the definition of Data Scientist over the past 5 years? Does it still exist or has it morphed into a new version of its old self? Learn more about the recent trends in job descriptions and salaries for data scientists, ML engineers, and others to best understand the best fit for your career trajectory and interests.
Hello I’m Jason
I work as a data scientist (which we will define more later in this article) in Silicon Valley, and I love to learn new things!
Introduction
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 confusion and inconsistent job functions during its growth. For example, there are seemingly many different titles with the exact same roles or the same titles with different roles:
Analytics Data Scientist, Machine Learning Data Scientist, Data Science Engineer, Data Analyst/Scientist, Machine Learning Engineer, Applied Scientist, Machine Learning Scientist…
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 branchingand 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.
In this article, we will first look into the overall trend of the data science industry and then compare ML engineers and data scientists in more depth. I do not mean to provide an extensive history but rather narrate what I have seen and experienced while living in Silicon Valley as a data scientist. Even when I wrote my article How to Data Science Without a Degree in 2017, my perspective on data science was very different.
Last year, I covered this topic when I was invited to give a short talk to data science students at Metis Bootcamp. I want to use this opportunity to explain the differences and help you find the role that suits you best. Let’s find out if this industry is still booming or ending with data, because that is what data scientists do, right? (Maybe not). Regardless, I hope you find it useful and informative.
#2020 jun opinions #career #data scientist #machine learning engineer
