To summarize, here are the main differences between data scientists and machine learning scientists: * Data scientists: Python, R, statistics, visualization tools, perhaps faster paced, simpler use cases * Machine learning scientists: software engineering, research-focused, perhaps slower paced, more involved use cases With that being said, I encourage you to explore both positions - in not just articles, but also in job descriptions themselves as that is where you will get the most accurate and applicable information, especially per company. I have personally seen these differences myself, however, that does not mean that the same will be true for you. I wanted to post this article as well in hopes that people will comment on their experiences in either role, as I know these two roles to some companies and employees may be exactly the same or vastly different. There is a bit of overlap, as well as some clearer differences. This article has outlined some of the differences and similarities between these two roles. Also, in some job descriptions, we will see that there might be a specialization like Physics or Robotics. The skills required for machine learning scientists tend to include more software engineering focus, like C++ for example, as well as more automation and deployment focused.
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