While their roles and responsibilities may vary, here are a few Data Science posts:
Business Analysts with a data focus: They are knowledgeable in specific business domains and are focused on exploratory data analysis.
Machine Learning Engineers: Software developers who build ML models based on the data. They have a good understanding of software architecture, ML engineering, and model development and deployment.
Domain Expert Data Scientists: Domain experts who are focused on the generating of the right features from the data given to answer questions. Not as skilled as machine learning engineers or statisticians.
Data Visualization Specialists: These Data Scientists create visualizations and graphs based on data. They work with BI tools, coded up scripts and programs, etc. for the purpose of data analysis
Statisticians: Analysts who build models of various kinds like factor-response models, distribution models, significance testing, DOE, etc. They are knowledgeable in machine learning and deep learning. They don’t handle large data sets.
Data Engineers with data analysis skills: Data Engineers are focused on developing data management infrastructure, data management systems, and pipelines for the implementation of models. They work with data ingestion, data lakes, data extraction, etc. They don’t work so much with data analysis despite having some understanding of the domain.
Data Science Managers: They are experienced Data Engineers or Data Analysts who focus on the deployment and use of data science results. They put together systems, processes, and tools for effective performances by the team of Data Scientists, Analysts, or Engineers.
To learn more about Data Science, here is a tutorial.
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