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This question is about what a data scientist does and data scientist.
Machine Learning Engineer vs. Data Scientist
A machine learning engineer is someone who writes low-level code, while a data scientist is a person who writes high-level code.
A machine learning engineer is a tech professional that writes low-level code to tweak and optimize default implementations. They are also proficient in high-efficiency programming languages such as C++, Java, and Scala. Machine learning engineers focus on scaling models into production while ensuring adherence to business service level agreements.
A data scientist is a tech professional that writes high-level code with Python or R. They often also use BI tools for data analysis and visualization. Data scientists are builders tasked with analyzing and understanding specific business problems, feature engineering, developing, selecting, and tuning models, and then generating insights to present to stakeholders.
Here are the key differences between a machine learning engineer and a data scientist:
They can use different software programs to perform the functions of their roles
Data scientists have to use creativity more than machine learning engineers
Machine learning engineers have more fundamental software skills
Data scientists interface with stakeholders directly
Machine learning engineers do not interface with stakeholders
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