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I would not advise anyone to go down the "data science" career path at this point, unless one of these topics is a true passion of yours and you can't imagine d

I would not advise anyone to go down the "data science" career path at this point, unless one of these topics is a true passion of yours and you can't imagine doing anything else (and even then I would recommend alternatives if possible). The explosion of AI/ML/Data Science teams in places that they really don't belong is going to have major backlash soon. The market is currently flooded with desperate PhDs who have reduced years studying their field into a flurry of buzzwords on a resume that they don't understand. The vast majority of AI/ML/DS teams don't know what they're doing. Management and "leadership" in this industry would be laughable if it wasn't so horrific. The vision of AI/ML/DS has turned into a nightmare of people shoving data they don't understand into models they don't understand and making sure someone else is using that so they can justify their work. You could likely cut entire 100+ DS teams at most companies today and only see a positive impact on the business. There are of course really interesting niches that require these skills and skilled practitioners. But if you're reading a guide on how to be an "AI Expert", they you likely aren't one of these people. And because of the signal noise problem in this space it's hard for even really excellent people to get matched to the hard problems that genuinely need their help.

pradn 6y ago
I wonder what the impact of easy-to-use cloud ML tools is going to be on the industry. BigQuery already lets users train models using a simple SQL query. AutoML makes simple ML tasks as easy as run and verify accuracy and run again. Of course, a basic amount of knowledge is still need to avoid common pitfalls - unclean data, overfitting, etc. Perhaps this will just mean data scientists can produce better work with the same amount of time. It doesn't seem like a good use of everyone's time to have to know so much cutting-edge research to solve run-of-the-mill business problems. Hardly any other area of CS requires that.
- deleted 6y ago
  [deleted]
6gvONxR4sf7o 6y ago
For readers who read that and think they still want to do it: > The vision of AI/ML/DS has turned into a nightmare of people shoving data they don't understand into models they don't understand... Don't be this ^. Please. If you do want to come into the field, please first take the time to understand the tools. Learn enough to re-derive and re-implement the tools you're using. Learn the assumptions that need to hold. Don't just learn to point the tools at data and hit go. It's not like a lot of programming where it either succeeds or fails. Failures are often silent in this field. If you can't reason through how and when a given tool will succeed and, probably more importantly, reason through how and when a given tool will fail, you haven't learned enough to know what to use and when.
- cambalache 6y ago
  I totally agree with you, but let's be honest, in this publish-or-perish world in academy, and the cut-throat environment of VC-funded start-ups, people will, if not outright lie, exaggerate the nature and importance of what they are doing. Too much money and "prestige" is at stake. The "right" way is slow, lonely and unglamorous. And most importantly without that fast money, acquisitions and promotions.
arminiusreturns 6y ago
I would say, however, there is ample opportunity for infrastructure designers/engineers in the space, especially in a world where everyone is going "cloud" but cloud doesn't cut it in the most demanding applications, so those of us who love data center design are sort of loving the resurgence of onprem/Colo usage and the opportunities for pushing the envelop on data transfer/processing rates, etc. The skills and knowledge gained are very useful in other industries, with a side bonus of, if you pay attention, knowing a bit more about the "big picture" of AI than many of the phd's!
nshm 6y ago
Every time i fire a data scientist the earnings grow
ta1234567890 6y ago
> The explosion of AI/ML/Data Science teams in places that they really don't belong is going to have major backlash soon I'm not disagreeing with you, just curious, did that happen with Big Data teams? If not, what happened with all the people working around that concept a few years ago? Might be an indicator of what could happen to DS teams.
- NhanH 6y ago
  I have heard hiring managers explicitly saying that data engineer role on your resume is considered a negative point for them.
morelandjs 6y ago
There’s a lot of truth in this, but I find the people that pivot toward practical software engineering skills early in their PhD are still highly valuable to tech companies. I think the issue here is you can’t become highly skilled in software engineering overnight, and compensating by flexing softer “problem solving skills” isn’t enough to compensate. If you want to be effective in data science, you need to commit to being a rockstar programmer AND statistician AND be well versed in a number of different algorithms. Unsurprisingly, this is difficult, and so the market of people who do it well is small. That’s not, however, a referendum on the value of predictive modeling and software engineering. I don’t see advanced math and software engineering skills going out of fashion anytime soon, simply because they are hard to acquire and immensely powerful in the right hands.

pradn 6y ago

I wonder what the impact of easy-to-use cloud ML tools is going to be on the industry. BigQuery already lets users train models using a simple SQL query. AutoML makes simple ML tasks as easy as run and verify accuracy and run again. Of course, a basic amount of knowledge is still need to avoid common pitfalls - unclean data, overfitting, etc. Perhaps this will just mean data scientists can produce better work with the same amount of time. It doesn't seem like a good use of everyone's time to have to know so much cutting-edge research to solve run-of-the-mill business problems. Hardly any other area of CS requires that.

deleted 6y ago
[deleted]

6gvONxR4sf7o 6y ago

For readers who read that and think they still want to do it: > The vision of AI/ML/DS has turned into a nightmare of people shoving data they don't understand into models they don't understand... Don't be this ^. Please. If you do want to come into the field, please first take the time to understand the tools. Learn enough to re-derive and re-implement the tools you're using. Learn the assumptions that need to hold. Don't just learn to point the tools at data and hit go. It's not like a lot of programming where it either succeeds or fails. Failures are often silent in this field. If you can't reason through how and when a given tool will succeed and, probably more importantly, reason through how and when a given tool will fail, you haven't learned enough to know what to use and when.

cambalache 6y ago
I totally agree with you, but let's be honest, in this publish-or-perish world in academy, and the cut-throat environment of VC-funded start-ups, people will, if not outright lie, exaggerate the nature and importance of what they are doing. Too much money and "prestige" is at stake. The "right" way is slow, lonely and unglamorous. And most importantly without that fast money, acquisitions and promotions.

arminiusreturns 6y ago

I would say, however, there is ample opportunity for infrastructure designers/engineers in the space, especially in a world where everyone is going "cloud" but cloud doesn't cut it in the most demanding applications, so those of us who love data center design are sort of loving the resurgence of onprem/Colo usage and the opportunities for pushing the envelop on data transfer/processing rates, etc. The skills and knowledge gained are very useful in other industries, with a side bonus of, if you pay attention, knowing a bit more about the "big picture" of AI than many of the phd's!

nshm 6y ago

Every time i fire a data scientist the earnings grow

ta1234567890 6y ago

> The explosion of AI/ML/Data Science teams in places that they really don't belong is going to have major backlash soon I'm not disagreeing with you, just curious, did that happen with Big Data teams? If not, what happened with all the people working around that concept a few years ago? Might be an indicator of what could happen to DS teams.

NhanH 6y ago
I have heard hiring managers explicitly saying that data engineer role on your resume is considered a negative point for them.

morelandjs 6y ago

There’s a lot of truth in this, but I find the people that pivot toward practical software engineering skills early in their PhD are still highly valuable to tech companies. I think the issue here is you can’t become highly skilled in software engineering overnight, and compensating by flexing softer “problem solving skills” isn’t enough to compensate. If you want to be effective in data science, you need to commit to being a rockstar programmer AND statistician AND be well versed in a number of different algorithms. Unsurprisingly, this is difficult, and so the market of people who do it well is small. That’s not, however, a referendum on the value of predictive modeling and software engineering. I don’t see advanced math and software engineering skills going out of fashion anytime soon, simply because they are hard to acquire and immensely powerful in the right hands.