Is domain knowledge necessary for a data scientist?

Data Science, AI and ML
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#1
Students and aspirants in the field of data science must be cognizant of the fact that the acceptance of their statistical models and output depend largely on their relevance to existing business problems. Therefore, it is important for data scientist to understand the actual problems faced by business & their priorities. And, the projects and analysis done should correspond or cater to those business problems – for example, a student from finance background would have sound knowledge on accounting concepts which would help him/her to understand the financial problems faced by businesses. Hence, they could cater their reporting solutions to correspond to such problems.
#2

  1. Having problem domain expertise is not necessary for a data scientist, unless you are doing research at an American outpost in Kandahar Province and the locals are out to get you.

  2. Having ACCESS to problem domain expertise is mission-critical for a data scientist. Your credibility as a professional is your bread and butter and you don’t want to accidentally destroy it by drawing unwarranted inferences from the data.

Having a problem domain expert support a team of data scientists should be enough due diligence.