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This question is about what a data scientist does and data scientist.
ML Engineer vs. Data Scientist
ML engineers typically write low-level code to tweak and optimize default implementations, while a data scientist writes higher-level code and often uses BI tools for data analysis and visualization.
ML engineers are proficient in programs such as C++, Java, and Scala but typically write code in more low-level programs. They tend to have more fundamental software engineering skills and sit at the crossroads between IT and data science. They have a strong foundation in data structure, algorithms, and creating deliverables.
Data scientist is often required to be more creative in their day-to-day tasks as they need to use their data to tell a story. They work with stakeholders directly and need to know how to present insights and solutions. They often use Python or R to write higher-level code.
There are many differences between an ML engineer and a data scientist, including salary expectations, education, and responsibilities.
Here are the key differences between an ML engineer and a data scientist:
An ML engineer is more focused on machine learning and data analytics
A data scientist is more focused on coding and algorithms
An ML engineer averages $132,000 per year
A data scientist averages $109,000 per year
An ML engineer requires a Bachelor's degree in machine learning or engineering
A data scientist requires a Bachelor's degree in data analytics or statistics
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