Cassie Koyzrkov is chief decision scientist at Google. In a article in HBR:
author is talking about important dilemma companies in AI world will have and that is what kind of role their data people will have and do they aim for generalist that will have all three abilities:
- analytics
- statistics
- machine learning
or they go for specialist and if so, how they should stack them and define their roles. Author is defining trend of looking for machine learning specialist, that can solve all problems, but instead points out that statisticians and machine learning specialist are narrow-and-deep workers and that if you define problems that are not worth solving, you will end up wasting time and money. So for companies to identify right problems you need analyst with their ability to quickly surf through data and gave insights to send decision makers in right directions. With help of analyst decision makers can select valuable quests to send statisticians and ML engineers on. Author even sees development of analyst with key capabilities of statisticians and machine learning specialist and suggest to companies, if they want to start with hiring and HR development in area of data handling, they should start with data analyst.
In very brief classification you have you analyst to identify possibilities, statisticians to verify them and machine learning specialist to construct tools to cope with them.
