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> For those of you who are new here (aka didn't live through the 2000 bubble) welcome to a shitty job market. Just as we saw in 2001, the market is worst for (a) recent graduates who have not yet established a professional network and (b) people who had poured into fungible commoditized job roles during the boom and were a dime a dozen when the bust came. (b) is roughly "programmer/analyst" in 2001, "Java developer" in 2008, and "data scientist" today. It's not that some people working under those titles don't have non-fungible skills, but that there are so many others with that job on their resume, that it's hard to differentiate. My advice is, _don't go looking for those jobs._ The opening anecdote in the article describes how terrible it is to try to get hired as a data scientist right now. And no wonder. The job market is flooded with low-quality data scientists. My experience working with data scientists has drastically lowered my expectations: you get a mediocre Python programmer (you must be willing to accept Pandas + Jupyter notebooks as their work output), combined with a mediocre statistician (can do linear regression and ANOVA, but don't expect them to do it right), combined with a mediocre SQL programmer (probably SELECT, maybe JOIN if you're lucky), and a mediocre machine learning specialist (has a list of preferred sklearn functions in random priority order depending on training, plus maybe one other random library that stops working in the middle of the project due to an API change). They made up for their cheap salaries by spending lots of money on AWS. Maybe I just got unlucky. Didn't know how to hire well. I'm sure there are good Data Scientists out there, but it's so easy to hire bad ones that it's no wonder people are reluctant to do it. If you're one of the good ones, and if you're in any way qualified for a job with a higher barrier to entry, or even just a job with a more unusual title than Data Scientist, go for that instead of trying to stand out in a sea of mediocrity. |