I don’t hire Junior data-scientists
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I've worked with some absolutely top-notch candidates straight out of college, and some mediocre ones which plenty of experience.
Noted.
- Train them yourself and then pay them a fair market value (that means actually giving them a raise!), or - you hire them from elsewhere.
Saying that you don't put effort into training tells me there are worse underlying issues, and I certainly don't want to work for you.
I work for a company that puts a lot of effort in training and education into their workforce (they paid for my BS and paid my salary while I got my MS). For gripes that I have about working for them, they take care of their own, mentor their, and make sure they are fairly compensated (to include raises). I have been working for them for the past 8 years, and I intend on working for them for the foreseeable future. That doesn't even mention that they also give me a great work life balance, and want to see me work up the ranks internally.
Seeing articles that only reenforce that I have it pretty good where I work.
I can hire an analyst or engineer and quickly they will be able to learn, get good at something and deliver value. After 2 years they can easily grow into a data-scientist with the good base they have had from their previous positions.
Some organization essentially rebrand analysts positions to "junior datascientist", but I believe this make it a mismatch of expectation for graduates getting out of school.
;)
I don't disagree that large amounts of people graduating from computer science programs are under-equipped to enter the workforce, but maybe focus on the shortcomings of the educational system than the people coming out of it.
They are generally ill equipped, but do not regard position such as analyst and engineers which would give them the foothold they would need to enter that position after a couple of years.
What they have to learn goes far beyond coding and entails getting a sense for data, the business sense that goes with it, an understanding of how to put models, etl code etc.. into production...
Most is fairly hard to teach in a classroom and rather requires practical experience in a business context, so I think the issue is more of an expectation issue than an educational issue. Although it is partially exacerbated by programs such as "Msc of DataScience", that makes student believe they would be ready for these positions straight after graduation.
You hire juniors and give them as much work as they can do with supervision.
I think the question there is "how fast"?
"You hire juniors and give them as much work as they can do with supervision." - Agree to a large extent, the main problem there is that their expectations don't usually match what they are ready for.
I believe that we should be focusing much more on pragmatic application of knowledge through apprenticeship in the later years of school.
The goal being to alleviate the feeling that you need to immediately train someone who just got done with 4+ years of training.
Often you would be better off having a first look at the input to figure out which case you should tackle, decompose the nested try/excepts into separate function or at the very least keep it to two nested levels.
If you can't grow people at your company then no one should work for you. That simple.
It's ridiculous. If you can't mentor someone at the job, then that speaks to your failures. It's not the job that's the problem.
A lot of firm, rebrand analysts jobs to be junior data-scientists position to essentially attract candidates, so yeah you can be "junior" at it, but it's essentially a different job.