This sentiment is quite popular among those who would like to have the same popularity that data scientists currently (well, more a few years ago, since there a

This sentiment is quite popular among those who would like to have the same popularity that data scientists currently (well, more a few years ago, since there are many more critical voices now) have, but they don't. Data science is a generic name. There are DS like me who have been "actual scientists" and others who until yesterday were working on dashboards and Excels files with 100 tabs open and pivot tables as far as the eye can see. Whatever, it is a name. What about "engineers"? It is a title with no legal value, people in the US can call themselves software engineers, but in many other countries, they could not. And who is a writer? Somebody making a living out of writing, somebody who has been published even if they got zero money for it and the magazine editor was their cousin, or else? People in my team do causal modeling, use reinforcement learning for network configuration, NLP for chatboxes, computer vision for face ID, and (again) network configuration. They are all called data scientists. Thinking that what people who have the title "Data Scientist" do is "generating value via increased consumer retention" or "ETL for Excel files for the boss" is between misinformed and laughable, but mostly laughable. The world is much bigger than that. Then, I agree that "learning from data" as a specialty has been over-hyped, and most companies do not have the maturity to take advantage of ML prediction, causal and statistical modeling, etc., but that's the nature of the world: one can take advantage of it or being bitter about it. I took advantage of the hype and I am fine, happy, and with no regrets. If tomorrow someone would propose to use for the same job the title "Data Monk" and it paid more, were more visible, and led to more career opportunities, I would grab it as quickly as I would grab 100 dollars floating in and out of the sidewalk.