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Agreed. Further, I personally think this is the issue at the core of the "data science shortage" right now. "Data science" means many things to many people. So

Agreed. Further, I personally think this is the issue at the core of the "data science shortage" right now. "Data science" means many things to many people. Some shops want BI folks who can code. Others want people who can keep up in deep learning threads (like the one on HN's front page) and know about Hinton's work. Others still want HDFS badasses who can distinguish between Cascading and Cascalog (lol). Shops classify all of these things as "data science" when they're really very different (respectively: Excel/scripting BI analyst; ML researcher with a focus on deep learning and neural networks; data infrastructure developer with broad exposure to MR frameworks) This is stupid. I worried about checking off these boxes until I realized recruiters are doing the 1990s Java thing all over again. This time, though, the buzzwords are Hadoop, information retrieval, and Andrew Ng. I think it's best to ignore this, keep working hard, and grow your employment options.

eshvk 14y ago
I agree with you: However, I am unsure who is at fault here. There is a unique problem in that people don't genuinely know what to call the role they want to hire for and there is a worry among recruiters that the "wrong" or "unsexy" job title will put off potentially amazing candidates. E.g. In Linkedin, the data science position for the most part could be more accurately called a Machine Learning Engineer. There are other companies where the data science role could be called a Statistician (As in someone who has advanced degrees in Statistics but who for all they care can't write anything but SQL). On the other hand, I have never ever found the requirements for a role to be nothing but mostly bullshit. So they don't really help you much. Your best bet if you are not going through a referral is to attempt to get to a phone screen where the Engineer/Data Scientist will be kind enough to spare some time at the end of the interview to tell you what on earth they are actually looking for. You could get lucky and learn just enough to terminate the interview process at this time. The worst situation is where you go there and realize that either a) You are not what they are looking for which is a half a day wasted or even worse b) They have not completely realized what they are looking for. Me? I have made a decision that I will either apply for software engineering positions where I will maybe be one of the Machine Learning folks or apply to data science positions only if I know some one in the team who can explain to me what on earth that company is actually looking for.
Jach 14y ago
To add, I don't think it's usually so much that the buzzwords are useless things to learn or nebulous (though some probably are both), but it's that companies aren't willing to pay employees to learn the buzzwords on the job (or even figure out which subset is actually relevant to the job--YAGNI is a useful heuristic here). They'd rather instead complain about CS programs being inadequate. Then when CS programs half-assedly try to change into learn-this-software dev-mills, they get complaints from another side of the field that they're skipping out on important theoretical background that good programmers tend to have even if they don't use it much. There are a lot of advertisements offering jobs only to people with "10+ years experience in X" (sometimes "X" hasn't even been a thing for that long), and assuming that means professional experience, that rules out almost everyone younger than 32. And for specific "X", it can rule out people even older if the "X" isn't what a recent graduate started getting their professional experience in right away. Some of those companies might also offer junior jobs with junior pay, but there are all these startups elsewhere offering higher-than-junior pay plus equity plus (depending on the size) a feeling of larger influence and control without the stress of running a company yourself. I'm not sure I agree with you on ignoring buzzword-laden fields since there's usually a lot of money to be made, even if only in the short term. Though it's probably best not to greedily expand your employment options in the direction of the latest buzz.

eshvk 14y ago

I agree with you: However, I am unsure who is at fault here. There is a unique problem in that people don't genuinely know what to call the role they want to hire for and there is a worry among recruiters that the "wrong" or "unsexy" job title will put off potentially amazing candidates. E.g. In Linkedin, the data science position for the most part could be more accurately called a Machine Learning Engineer. There are other companies where the data science role could be called a Statistician (As in someone who has advanced degrees in Statistics but who for all they care can't write anything but SQL). On the other hand, I have never ever found the requirements for a role to be nothing but mostly bullshit. So they don't really help you much. Your best bet if you are not going through a referral is to attempt to get to a phone screen where the Engineer/Data Scientist will be kind enough to spare some time at the end of the interview to tell you what on earth they are actually looking for. You could get lucky and learn just enough to terminate the interview process at this time. The worst situation is where you go there and realize that either a) You are not what they are looking for which is a half a day wasted or even worse b) They have not completely realized what they are looking for. Me? I have made a decision that I will either apply for software engineering positions where I will maybe be one of the Machine Learning folks or apply to data science positions only if I know some one in the team who can explain to me what on earth that company is actually looking for.

Jach 14y ago

To add, I don't think it's usually so much that the buzzwords are useless things to learn or nebulous (though some probably are both), but it's that companies aren't willing to pay employees to learn the buzzwords on the job (or even figure out which subset is actually relevant to the job--YAGNI is a useful heuristic here). They'd rather instead complain about CS programs being inadequate. Then when CS programs half-assedly try to change into learn-this-software dev-mills, they get complaints from another side of the field that they're skipping out on important theoretical background that good programmers tend to have even if they don't use it much. There are a lot of advertisements offering jobs only to people with "10+ years experience in X" (sometimes "X" hasn't even been a thing for that long), and assuming that means professional experience, that rules out almost everyone younger than 32. And for specific "X", it can rule out people even older if the "X" isn't what a recent graduate started getting their professional experience in right away. Some of those companies might also offer junior jobs with junior pay, but there are all these startups elsewhere offering higher-than-junior pay plus equity plus (depending on the size) a feeling of larger influence and control without the stress of running a company yourself. I'm not sure I agree with you on ignoring buzzword-laden fields since there's usually a lot of money to be made, even if only in the short term. Though it's probably best not to greedily expand your employment options in the direction of the latest buzz.