That's not a counterargument to his point. You're parsing job titles down to the atom, and concluding that "data scientist" is different than "scientist" is different than "statistician", is different than "analyst". Gaius is saying that this job responsibility has been around for a long time, but that people are reaching to find reasons to give it a new name -- exactly what you're doing.
If you ask me, the phrase "data scientist" is recruiter-speak. I have all of the skills required of a "data scientist". I've done the job of a "data scientist". And other than object recognition, I've developed all of the different product features you mention in your comment. You know how I got the skills necessary to do those things? I was trained as a scientist, and there's no such thing as a scientist without data. A person properly trained to analyze data should be able to effectively and fluidly transfer those skills between domains -- otherwise, they're not actually good at it. There's nothing special about internet products that precludes competent people from doing effective data mining on their logs.
I suspect that the real problem here is that "data science" is Internet Hipster for: "someone who has already worked at an internet company, and knows some statistics". Because when it comes right down to it, your average statistician, chemist or physicist is more skilled at data analysis than 99.9% of the "data scientist" types you meet, but they don't easily press the comfort button for hiring managers at consumer internet companies. Why hire the "risky" ex-scientist, when you can hire the guy who claims to be a designer, a software engineer and a statistician?
I agree that data scientist does smell a lot like recruiter/marketing speak. But on the other hand, just because it's a new title doesn't mean it isn't valid. Reducing everyone down to "Scientist" is no more helpful than saying a physicist isn't really a separate job, but just a specialised branch of mathematics. Or for that matter, CS is just a narrow branch of mathematics.
Eventually you have to distinguish new fields from the old, even if they have a lot of commonalities.
Well, yeah...when there's specialized knowledge required for the job (like, say, "physics"), it's obviously a good idea to change the job title.
The problem here is that "data scientist" adds no semantic value above and beyond "scientist". A scientist of data, you say? However will we find such exotic creatures!?
If you ask me, the phrase "data scientist" is recruiter-speak. I have all of the skills required of a "data scientist". I've done the job of a "data scientist". And other than object recognition, I've developed all of the different product features you mention in your comment. You know how I got the skills necessary to do those things? I was trained as a scientist, and there's no such thing as a scientist without data. A person properly trained to analyze data should be able to effectively and fluidly transfer those skills between domains -- otherwise, they're not actually good at it. There's nothing special about internet products that precludes competent people from doing effective data mining on their logs.
I suspect that the real problem here is that "data science" is Internet Hipster for: "someone who has already worked at an internet company, and knows some statistics". Because when it comes right down to it, your average statistician, chemist or physicist is more skilled at data analysis than 99.9% of the "data scientist" types you meet, but they don't easily press the comfort button for hiring managers at consumer internet companies. Why hire the "risky" ex-scientist, when you can hire the guy who claims to be a designer, a software engineer and a statistician?