Credible people will do one or two competitions because they care about the problem, and because they want to establish themselves well enough to get better jobs, etc. If it works for them, great. If it doesn't, they'll get bored and quit. In that case, the best people leave and you have a ghetto.
Right now, Kaggle makes sense because "data science" is still an ill-defined field but a lot of people want to get into it, and no one knows what it means or takes to get in, so people will try things out to see what happens.
If Kaggle wants to stay in play for the long term, they'll need to get really good at connecting talented people with very high-quality jobs.
There is something that I don't think all of the hiring-related startups get yet: as things are, there's such a shortage of quality jobs. That's a 5-year existential threat to the whole business model. What happens when people realize that these sexy startup jobs are just corporate jobs with better marketing? What happens when the dream dies? Right now, high-quality jobs are too rare for the hiring startups (unless the genuinely change the economy) to prevent people from getting just as disillusioned with these new services as they are with headhunters. Now, that's not because there's an intrinsic limit on interesting work (see: Lump of Labor fallacy) but we'd have to overthrow the management of a whole industry to change that.
Now, data science. It is attractive right now because it carries with it a promise of what software engineering was supposed to deliver but, for most people, doesn't: interesting work, implicit respect, autonomy. I feel like data scientist in many companies means "software engineer who gets dibs on the most interesting projects". I'm afraid that title inflation into the data science field might dilute that, however.
What we really need is to fire 90+ percent of software managers and trust engineers to pick their own projects and call some shots. I don't know how to turn that into a specific startup idea, but it will solve a lot of problems.
Credible people will do one or two competitions because they care about the problem, and because they want to establish themselves well enough to get better jobs, etc. If it works for them, great. If it doesn't, they'll get bored and quit. In that case, the best people leave and you have a ghetto.
Right now, Kaggle makes sense because "data science" is still an ill-defined field but a lot of people want to get into it, and no one knows what it means or takes to get in, so people will try things out to see what happens.
If Kaggle wants to stay in play for the long term, they'll need to get really good at connecting talented people with very high-quality jobs.
There is something that I don't think all of the hiring-related startups get yet: as things are, there's such a shortage of quality jobs. That's a 5-year existential threat to the whole business model. What happens when people realize that these sexy startup jobs are just corporate jobs with better marketing? What happens when the dream dies? Right now, high-quality jobs are too rare for the hiring startups (unless the genuinely change the economy) to prevent people from getting just as disillusioned with these new services as they are with headhunters. Now, that's not because there's an intrinsic limit on interesting work (see: Lump of Labor fallacy) but we'd have to overthrow the management of a whole industry to change that.
Now, data science. It is attractive right now because it carries with it a promise of what software engineering was supposed to deliver but, for most people, doesn't: interesting work, implicit respect, autonomy. I feel like data scientist in many companies means "software engineer who gets dibs on the most interesting projects". I'm afraid that title inflation into the data science field might dilute that, however.
What we really need is to fire 90+ percent of software managers and trust engineers to pick their own projects and call some shots. I don't know how to turn that into a specific startup idea, but it will solve a lot of problems.