"Have you or somebody you know successfully made the transition from (non-computer) science into a tech career? In the current economy, what are the chances you'd hire a highly motivated science dropout with programming competence and basic stats knowledge?"
I did it, have worked with others who have done it, and would now hire people who did. The important part is your ability to program. There are tons of PhD students (even in CS), who can't (or won't) write code. There are even more who write horrible, unmaintainable code. You have to be better than those folks.
Also, do not be deceived by "data science": it's mostly a bullshit term, and translates roughly to "programmer who knows basic statistics", rather than "scientist who knows some programming". Nobody wants to hire you if you can't implement your theories in a production context.
The bottom line is that if you're a good coder, nobody cares how you wasted your youth.
Also, do not be deceived by "data science": it's mostly a bullshit term, and translates roughly to "programmer who knows basic statistics"
This is utter rubbish; I really wish people would keep quiet about things they know nothing about. I suggest that you have never actually discussed a domain with a data scientist if you think it's "basic statistics".
In our dev. shop, we have a lot of great programmers, but none of them can touch our data scientist when it comes to working out what our tens of millions of users are actually doing and what their salient attributes are.
As for the data scientist needing to 'implement their theories', that's what the developers are for. The data scientist does the analysis, then works with the developers to implement systems that incorporate the results. Neither group is capable of the other's work.
"I suggest that you have never actually discussed a domain with a data scientist if you think it's 'basic statistics'."
Utter rubbish, perhaps. But since I've actually done the job, I do happen to know something about the subject. It's a marketing term, not a term of art.
The vast majority of "data science" performed at web companies boils down to knowledge of summary statistics and probability theory, a smattering of basic statistical models, and (most importantly) the ability to write code. There's not much that would challenge an advanced undergraduate, let alone a doctoral-level statistician.
I did it, have worked with others who have done it, and would now hire people who did. The important part is your ability to program. There are tons of PhD students (even in CS), who can't (or won't) write code. There are even more who write horrible, unmaintainable code. You have to be better than those folks.
Also, do not be deceived by "data science": it's mostly a bullshit term, and translates roughly to "programmer who knows basic statistics", rather than "scientist who knows some programming". Nobody wants to hire you if you can't implement your theories in a production context.
The bottom line is that if you're a good coder, nobody cares how you wasted your youth.