81–90 of 140 posts
If you're hiring: Get the above out of your pathetic small minds and start hiring the smartest people you can find. Look for successes in any industry. Your business isn't that unique. The best people can learn it much faster than you did.
Currently, Gartner analysts place ML at the "peak" of its Hype Cycle for Emerging Tech [0] with a runway of 2-5 years for mainstream adoption. [0] http://www.gartner.com/newsroom/id/3412017
that must be qualified because there are a lot of ML applications that have basically been adopted in the mainstream, like voice recognition (which is pretty good, probably in the 80-90% accuracy range and better for specialized contextual tasks). I remember driving for lyft and being able to input destination addresses by voice (2 years ago; had a Moto X where this was cutting edge) was a godsend and improved my cus…
For continuous use, you need 95% at least. See http://anewdomain.net/2014/01/28/lamont-wood-windows-speech-...
Earlier quoted context omitted.
Building a career around a framework is never a good idea. If you know your shit, it shouldn't matter what framework you're using. Theano and TF, for example, both make similar abstractions: graphs and numerical functions on top of the same matrix library, even. I would suspect someone could move between the two fairly easily. The problem is that a programmer can use TF/Theano/etc.'s built-in gradient descent functio…
If you know your shit, it shouldn't matter what framework you're using. And yet, what is the incessant drumbeat of most job ads, these days - even in data science? That's right: "N years in framework X"
Should most ML job postings require n years of experience in some framework? Maybe not - but I can see why a company might see value in it.
I'm hiring 6 people in a range of roles between "pure" data scientists to more data engineer/SWE roles. The exact mix depends on who we can get.
The ability to find good people is the biggest constraint on the work we do.
Our current team ranges from applied mathematicians (as in they are Math professors) to people with traditional SWE backgrounds. Basically we are a long long way from saturated.
I can't speak to anything regarding ML, but for whatever it's worth in our segment of the market we have seen a lot of competition emerge in a big way the last few years. Former academic-type firms who specialized in bespoke economy analysis reports are starting to build software around all of the data that is out there since it's never been easier to collect and normalize it. I think it's a stretch to say the market is approaching saturation for us, though.
Earlier quoted context omitted.
It seems that this is true for now (for 'traditional companies'). Soon, however, one could argue that 'traditional companies' will no longer be the norm - data science, ML, etc. will play such a crucial role in the majority of tech firms that the number of companies using it will rise. That's when I expect we'll see a huge portion of software engineers knowing ML concepts. Alternatively, I wonder if we might see the…
You need a friend at GooMAzonSoft to refer you. It's always been and will always be the easiest way in. Then traditional uninteresting phone call and uninspired 6 hours on site with people who probably didn't read your resume. Maybe if you have a good profile and you get lucky, you'll go interview straight for one group who's interested in you, but I wouldn't bet on that.
I'm an undergrad at a big university known for CS in Canada. The CS program here has several possible 'focuses'; 4 of 9 are related to ML/AI directly (computer vision, NLP, AI, scientific computing). 2 others require AI/ML/NN courses. The bias might stem from the fact that we have some huge names in AI doing research here, but the data points seem clear (we say undergraduate education is slow to catch on, right?): th…
> I'm an undergrad at a big university known for CS in Canada
than the actual name of the university?
> I'm an undergrad at ${name of university}
Is there a market for competent developers without professional/academic experience in data science or machine learning? Perhaps just a MOOC or some Kaggle projects?
Speaking for NYC, but I imagine silicon valley is similar. The supply-demand dynamics have changed a lot in the last couple years. I'd roughly break it out into two groups: people with work experience + strong software development skills, and those without. The first group is in higher demand than ever, and tend to add a lot of value to companies that really need it. The second group has gotten extremely crowded, esp…
I am the Chief Data Scientist of Dice.com. If you are interested in working as a junior Data Scientist, and are smart and hard working, please apply here: http://careeropportunities.dhigroupinc.com/ . The position is a telecommute role. We will absolutely consider people with no data science experience, so long as they demonstrate an aptitude for data science \ machine learning and can code.