Is “data scientist” the new “programmer”?

My current job title is "Data Engineer", before this role I was a "Process Engineer". In my opinion those two jobs are actually pretty similar.

When I was working in process engineering I was trying to optimize the outputs from our industrial process on a day to day basis in this role broadly speaking I try to optimize the data extracted from the same industrial process.

Mostly I'm concerned with how can we extract data out of our plant, how do we represent and present that data (particularly to operators and technicians) and how can we better recognize and respond to underlying trends in the data.

Before I assumed the role (in 2011) my predecessor, who had a background as a statistician, was called a 'Process Statistician' so I assume my Manager changed the job title to reflect my background as an Engineer (Materials Engineering in my case).

Yes, "data scientist" is the new "70's programmer"... write some code in one file that runs within a hosting system (mainframe, spark).

Regarding the complexity and tedium of many production code bases I think they got there because many developers don't have the ability (experience) or opportunity (iterations) to do things simply.

why?

The take down on abstraction and software engineers (by using Java as an example) is similar to saying "back in the day to find a prime number we would simply use a sieve, but today it is a tedium, what with all the pi's and e's and thetas that get in the way, and what are geometry and polynomials doing here, and what in the god's name is this i, I just want to count the prime numbers which are nice round whole numbers".

That's what happens when a topic grows from being a curiosity where dilettantes dabble into a proper field that is applied to solve problems. Granted, some of the developments can indeed be tedious and self indulgent, but otherwise this is the natural progression. Its sad and frustrating when people who ought to know better make such statements. Is it done to provoke a critical analysis, positive trolling if you will?

About the role of data scientist, I find it both amusing and disappointing that just about anyone with a three week MOOC gets to work in this field, who otherwise had never dealt with statistics before. I mean, statistics is a three year long grueling applied maths degree, and condensing it to three weeks is silly. It is actually in this way that it is similar to progamming job of the 90s (I don't know how it was in the 70's, I wasn't born yet). Just about anyone who could learn Java or VisualBasic, or the self taught cowboys who used C, ended up programming professionally. Actually it was not that bad, for coding is not as hard as its made to be, but that until they got sucker punched by the n squared complexity, to say the least, on a big data. Coding couldn't help them and they realized programming was more than learning to code and using some API's and system calls. (I was one of them in a way, when I started to code in C++ to model and simulate my mechanical engineering project, and it lead me to the path of enlightenment.) So, today's data scientists who are not bonafide statistics graduates or statisticians have it coming as well, whatever the analogue is, unless they are merely "data monkeys" in which case all is well and as expected.

Fascinating quote. Indeed misperception of job function is a problem here and in many fields.

I think you're right, though. Business people don't care much about statistical significance, so what's the point? We can just take the sample mean to see what is best. And recommendations? Whatever is most popular. What's contributing to the change in revenue? Let's look at the alignment of the planets