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This is fascinating stuff and well worth learning for its own sake but I think people that are jumping on this bandwagon with the idea that there is a bonanza o

This is fascinating stuff and well worth learning for its own sake but I think people that are jumping on this bandwagon with the idea that there is a bonanza of high paying jobs waiting for them are going to be disappointed. Even very heavily data-centric companies only hire a few ML specialists for every handful of general purpose code monkeys.

myth_drannon 11y ago
100% true, most of time companies hire PhDs only.
- pskisf 11y ago
  True from my experience as well. If you're going to invest in a Data Scientist, you want the cachet of having a Phd in that role.
- 0xdeadbeefbabe 11y ago
  Not startups.
- nowey 11y ago
  Ooyala has only 3 data scientists. All have PhDs and come from a math/statistics/com sci background. Kueski only has 1!
deleted 11y ago
[deleted]
donretag 11y ago
I have been working in teams with a big ML component for many years, but I have been working from "the outside". I keep on being promised to work more in depth, but never get a chance to. When job hunting last year, I noticed that at almost every company, the division being developers and those working on ML is almost 100%. Both roles are silo'd. Good luck transitioning from dev to ML if you have already started your career. It was not like this just 4 years ago.
ElysianEagle 11y ago
I think the same thing that happened to quants on Wall Street will happen to those pursuing Data Science/ML today (if it hasn't already happened). Post-2008 there was such a glut of qualified quants that companies moved the goalpost and now it's very difficult to even be considered for a role if you don't have a PhD. With so many programs and courses springing up about ML, in a few years I suspect there will be a glut of Data Scientists on the market, and once again companies will use the PhD as a filter. Already it seems like a good number of places will only hire PhDs for such roles.
- mindcrime 11y ago
  I think the same thing that happened to quants on Wall Street will happen to those pursuing Data Science/ML today (if it hasn't already happened). Post-2008 there was such a glut of qualified quants that companies moved the goalpost and now it's very difficult to even be considered for a role if you don't have a PhD. I'm not sure that's a valid comparison. There's a relatively fixed and fairly small pool of companies who need quants. Machine Learning, OTOH, can be used by almost any company in existence (even if most of them don't realize it yet). And plenty of companies don't need somebody doing cutting edge academic research in ML... they need somebody who can use a pre-packaged library or service and apply linear regression, or k-means, or build a simple neural network with backprop.
  - sbisker 11y ago
    Once it becomes straightforward to do ML with a pre-packaged library, you'll quickly start to see Amazon or a third-party offer effective MLaaS. That will suck quite a bit of oxygen out of the room.
    - nostrademons 11y ago
      Already exists: https://aws.amazon.com/machine-learning/ https://aws.amazon.com/machine-learning/ Problem is, writing an effective machine-learning model already doesn't require knowing the algorithms well. It requires knowing your data well. You can provide tools for this, and AML does, but there's no substitute for actually working with the data day-in-and-day-out and developing an intuition for it. (Deep learning promises to change that a bit, since the relevant features are extracted for you by the algorithm and you don't need to do any particular data cleaning or feature extraction work. You still need to understand your data well to understand how to train the model, though, and how to apply primitive ML operations - classification, regression, clustering, etc. - to a real-world problem.)
vram22 11y ago
Interesting. I sometimes mentor junior / aspiring devs on a site, and have seen that many of them seem to be trying to get on to the bandwagon you mention; some of them don't even have good experience with basic programming, and are still trying to do machine learning / data science MOOCs, bootcamps or courses on free sites. Some of them seem to be trying to learn programming at the same time as data science / machine learning. Don't think that is going to work out well for them. Many of them seem to in it for the supposed bonanza. Wanting to get a well-paying job is fine in itself, but many of them seem to be trying to shortcut the learning process, either due to ignorance or just because they don't want to spend a lot of time. Edit: grammar.
- vram22 11y ago
  Here's an example I came across on that site, that shows what I mean --- someone put in a request: "I want to build an advanced app and I don't know where to start." Hey Wow. Hoo Boy (or Girl). Maybe you should start by learning the basics, get some work experience, and only then try to pull a Zark Muckerberg or a Bacefook.
Futurebot 11y ago
Where would you go next if that sub-area is already saturated? I'd particularly like to get some opinions for self-taught devs tired of CRUD and DevOps land(s.)
- cageface 11y ago
  Good question. I think GOOD crud developers are still in high demand and probably will be for some time. As for myself I've been focusing on getting good at building complex user interfaces. Can't go wrong learning React now.
- arvinsim 11y ago
  Personally, I think that data visualization would be an interesting niche to go to.
arvinsim 11y ago
> This is fascinating stuff and well worth learning for its own sake Touché. As a web developer, I have no illusions on competing with people with the proper mathematical background. However, there are still some problems that are relevant and interesting in our field(collaborative filtering for instance) So I guess if you temper your expectations, you won't get burned.
arvinsim 11y ago
> This is fascinating stuff and well worth learning for its own sake Touché. As a web developer, I have no illusions on competing with people with the proper mathematical background. However, there are still some problems that are relevant and interesting in our field(collaborative filtering for instance) So I guess if you temper your expectations, you won't get burned.
kamaal 11y ago
>>Even very heavily data-centric companies only hire a few ML specialists for every handful of general purpose code monkeys. True for now, but it will change. Once terms like 'Statistics', 'ML' and 'Data science' will become common place, there will be a ridiculous amount of lust for automation jobs. Sure you won't be fixing the beta release bugs for skynet 1.0. But almost any job which has a scope for data collection, analysis and automation will demand skills in these area.

myth_drannon 11y ago

100% true, most of time companies hire PhDs only.

pskisf 11y ago
True from my experience as well. If you're going to invest in a Data Scientist, you want the cachet of having a Phd in that role.
0xdeadbeefbabe 11y ago
Not startups.
nowey 11y ago
Ooyala has only 3 data scientists. All have PhDs and come from a math/statistics/com sci background. Kueski only has 1!

deleted 11y ago

[deleted]

donretag 11y ago

I have been working in teams with a big ML component for many years, but I have been working from "the outside". I keep on being promised to work more in depth, but never get a chance to. When job hunting last year, I noticed that at almost every company, the division being developers and those working on ML is almost 100%. Both roles are silo'd. Good luck transitioning from dev to ML if you have already started your career. It was not like this just 4 years ago.

ElysianEagle 11y ago

I think the same thing that happened to quants on Wall Street will happen to those pursuing Data Science/ML today (if it hasn't already happened). Post-2008 there was such a glut of qualified quants that companies moved the goalpost and now it's very difficult to even be considered for a role if you don't have a PhD. With so many programs and courses springing up about ML, in a few years I suspect there will be a glut of Data Scientists on the market, and once again companies will use the PhD as a filter. Already it seems like a good number of places will only hire PhDs for such roles.

mindcrime 11y ago
I think the same thing that happened to quants on Wall Street will happen to those pursuing Data Science/ML today (if it hasn't already happened). Post-2008 there was such a glut of qualified quants that companies moved the goalpost and now it's very difficult to even be considered for a role if you don't have a PhD. I'm not sure that's a valid comparison. There's a relatively fixed and fairly small pool of companies who need quants. Machine Learning, OTOH, can be used by almost any company in existence (even if most of them don't realize it yet). And plenty of companies don't need somebody doing cutting edge academic research in ML... they need somebody who can use a pre-packaged library or service and apply linear regression, or k-means, or build a simple neural network with backprop.
- sbisker 11y ago
  Once it becomes straightforward to do ML with a pre-packaged library, you'll quickly start to see Amazon or a third-party offer effective MLaaS. That will suck quite a bit of oxygen out of the room.
  - nostrademons 11y ago
    Already exists: https://aws.amazon.com/machine-learning/ https://aws.amazon.com/machine-learning/ Problem is, writing an effective machine-learning model already doesn't require knowing the algorithms well. It requires knowing your data well. You can provide tools for this, and AML does, but there's no substitute for actually working with the data day-in-and-day-out and developing an intuition for it. (Deep learning promises to change that a bit, since the relevant features are extracted for you by the algorithm and you don't need to do any particular data cleaning or feature extraction work. You still need to understand your data well to understand how to train the model, though, and how to apply primitive ML operations - classification, regression, clustering, etc. - to a real-world problem.)

vram22 11y ago

Interesting. I sometimes mentor junior / aspiring devs on a site, and have seen that many of them seem to be trying to get on to the bandwagon you mention; some of them don't even have good experience with basic programming, and are still trying to do machine learning / data science MOOCs, bootcamps or courses on free sites. Some of them seem to be trying to learn programming at the same time as data science / machine learning. Don't think that is going to work out well for them. Many of them seem to in it for the supposed bonanza. Wanting to get a well-paying job is fine in itself, but many of them seem to be trying to shortcut the learning process, either due to ignorance or just because they don't want to spend a lot of time. Edit: grammar.

vram22 11y ago
Here's an example I came across on that site, that shows what I mean --- someone put in a request: "I want to build an advanced app and I don't know where to start." Hey Wow. Hoo Boy (or Girl). Maybe you should start by learning the basics, get some work experience, and only then try to pull a Zark Muckerberg or a Bacefook.

Futurebot 11y ago

Where would you go next if that sub-area is already saturated? I'd particularly like to get some opinions for self-taught devs tired of CRUD and DevOps land(s.)

cageface 11y ago
Good question. I think GOOD crud developers are still in high demand and probably will be for some time. As for myself I've been focusing on getting good at building complex user interfaces. Can't go wrong learning React now.
arvinsim 11y ago
Personally, I think that data visualization would be an interesting niche to go to.

arvinsim 11y ago

> This is fascinating stuff and well worth learning for its own sake Touché. As a web developer, I have no illusions on competing with people with the proper mathematical background. However, there are still some problems that are relevant and interesting in our field(collaborative filtering for instance) So I guess if you temper your expectations, you won't get burned.

arvinsim 11y ago

kamaal 11y ago

>>Even very heavily data-centric companies only hire a few ML specialists for every handful of general purpose code monkeys. True for now, but it will change. Once terms like 'Statistics', 'ML' and 'Data science' will become common place, there will be a ridiculous amount of lust for automation jobs. Sure you won't be fixing the beta release bugs for skynet 1.0. But almost any job which has a scope for data collection, analysis and automation will demand skills in these area.