Easy rule of thumb is to double the hourly rate to get a rough estimate of the yearly salary, so $30/hr ~ $60k a year. If a company where to hire an undergrad stats major they might pay $60k as a starting salary. Since the post is about freelance work they may have had someone in HR basically extrapolate this salary to ~$30/hr. Of course short term freelance work is going to work on a different pay scale, but there are plenty of examples where companies either try to get away with it or just plain don't understand the difference.
The "double the hourly rate" rule works from the employee's perspective -- $30 hr x ~2000 hrs worked in a year = $60k, but not from the employers, because the fully loaded cost of an employee with a $60k salary is probably over $100k.
There are graduate students doing the jobs of many industry professionals for $10-15 an hour. I think that says more about how poorly paid graduate students are than what the market rate for a data scientist should be.
That is the problem with those tier one rnd jobs is that the all salaries are so low across the board I worked on campus at CIT as a technician and I started on about 1/3 of what a Trainee nurse did (same entry requirements)
The majority of the time, the results are only worth $10-$15 an hour. Like the article said, the folks that can really produce the good results are worth much more than that.
Grad students are compensated in other ways: training, prestige, independence, etc. Almost everyone in a respectable CS PhD program could have gotten a $100k/yr tech job instead. The fact that they turned this down in favor of a $30k grad school stipend indicates that they value the intangible rewards of grad school at at least $70k/yr, i.e., way more than the monetary compensation. You certainly couldn't hire a grad student on the free market for $30k/yr without providing equivalent intangibles.
Could you expand a bit on the intangible benefits of a cs PhD? I have recently been offered admission to a top 10 program and am struggling to decided between it and staying in industry.
The marginal benefit of grad school is obviously a very personal thing. It depends on a) how excited you are about your current position/opportunities in industry, and b) how excited you are about the independent research you'd pursue in a PhD program. The latter will of course depend on what program(s) you're considering, who your likely advisors might be, and if there's a research topic you're passionate about investigating that you wouldn't otherwise be able to pursue in industry.
In my case, I was interested in machine learning, and after doing some internships as an undergrad concluded that there was no easy path to work on interesting ML problems in industry with only a bachelor's degree (both because the positions weren't available, and because I didn't know enough to do the work). So for me the first-order benefit is that I get to spend my time learning about and working on stuff that I'm interested in, and developing a skill set and a credential that should let me keep working on interesting things for the foreseeable future. It's different for other people, of course: some want to become professors, some want knowledge for knowledge's sake, some could pursue their chosen area equally well in industry but have a specific crazy idea they want the freedom to explore (I think this is more common in systems research), some want the lifestyle flexibility to go skiing in the middle of the week and make up by cramming work on weekends, etc. For most people it's really a mix of these factors and others.
The other main bit of grad school advice I have is that your relationship with your advisor is extremely important. The chance to be mentored one-on-one for six years by an extremely bright person you click with, share interests with, and who wants you to succeed is incredibly valuable. On the flip side, the possibility of being stuck for six years under a boss who doesn't care about you or know how to motivate you and develop your skills can be a massive liability (and one that's harder to escape than the equivalent situation in industry, since a lot of people internalize the value structure of academia and think of quitting a PhD as a failure on the part of the student rather than the advisor). So whatever you can find out about your likely advisors -- both by talking directly to them when you visit (are they looking for new students? do they seem enthusiastic about you specifically? how do they help their students find research topics? what is their philosophy of how to develop a student's skills as an independent researcher?), and by talking to their students: take them out to coffee and ask pointed questions like "is X a good advisor?", "what do you like/dislike about working with X?", "what sort of person is best suited to working with X?", "would you make the same choice again?" -- should have a huge bearing on your decision whether to attend, even if your program doesn't actually make you choose an advisor initially.
From the point of view of someone hiring/interviewing/having spent some time around PhDs, I'd say the main benefit is that it pushes you to be intellectually more capable. In a good university, doing original research, you'll be doing a lot of actual thinking and continuous learning and be pushed to do more of it than you'd do naturally. This is the main reason I wish I had done a PhD, although I don't think I'm smart enough to have gotten in a good programme (graduated with a 2.2 in engineering from Cambridge - all the MIT/Princeton/Stanford/whatever admitted had firsts).
It's in no way the sole predictor, but the probability of someone have done hard things and being able to solve tougher problems [1] quicker is higher with a PhD than someone who spent the same time in industry. It's also tremendously competitive (in the same way, say, Goldman Sachs is in "business") so you can trust the PhD admissions committee to have done some filtering for you. Finally, it takes a lot of willpower to stick through it til the end (particularly in the US with its much longer programs), and drive is a strong predictor of adding value to a company later.
On the downside, few people in business realise that because they haven't hung around PhDs or thought about it much, and just see a "lack of industry experience", an "attitude" and "not the most commercial mindset" (usually with a mention of the "ivory tower").
A good corporate job will have you learn a lot (broader, less specialist, probably less hard); but most corporate jobs are not good, and pay well to compensate the drop in intellectual challenge and loss of skills that results vs your undergraduate self (although you'll pick up more useful soft skills, to an extent, vs learning to navigate bureaucracies which are the soft skills a PhD teaches you). I heard that across practically all academic fields.
[1] Unfortunately most of the problems people actually need solved in tech companies, like building a data warehouse, some reporting, a user friendly front end, are not "tough problems" in the way, say, building a machine vision engine tailored to your specific industry might be.
> Grad students are compensated in other ways: training, prestige, independence, etc.
A grad assistant is usually not perceived as an employer by most universities, US and abroad. As such, from the university's perspective (which you took in your comment), one cannot say that they are being compensated, at all.
Assuming that you do understand that a graduate assistant (in this case, e.g. a research assistant who is also a student) is actually an employee of the university,
- The training they receive (courses et al) is what we call unpaid training. They do not receive salary for the hours they spend receiving training, which is a requirement for them to do their job (research assistant) efficiently and successfully.
- Would you accept payment in prestige if you were in, say, Google or Apple? A graduate school has much less prestige (hence the need for "internships").
- Independence does not exist for a grad assistant, whose not only livelihood but also school registration depends on her/his relationship with her/his "mentor" (i.e. boss / advisor to boss) and other department members. Not only job security but also school security depends on maintaining "good relationships" with these people. (As a student, on the other hand, s/he is expected to produce and engage in independent thought.) Additionally, this particular kind of employee cannot switch jobs easily since most jobs will not provide benefits for external education.
- No or lower-end benefits etc.
You can very easily hire a grad student on a "free market"[1] for even $15k/yr if they are [made to be] desperate enough. Most already are.
[1] Please read Adam Smith to understand how a "free market" actually operates. It's not what you think it is.
Graduate students who work for university as teachers are unionized at many schools. Research assistants might not be, on the theory that their research work is supervised education, not net value creation.
You're the one who doesn't understand the free market.
Grad students tend to be intelligent capable people (from privileged backgrounds too!). Therefore we don't expect grad students to be desparate, because we infer that they ahve a lot of other options.
The only sense in which grad students are desperate, is that they really want the other advantages that being a grad student confers, most of all the education and experience, and the degree they get at the end. And this was precisely the point of the post you replied to: graduate students are getting non-monetary compensation.
If you disagree, I challenge you to find one testimonial from a grad student whose story is that they were so desperate for money, that they had no choice but to enroll in grad school to make ends meet.
It's better to think of grad school as an apprenticeship, where you're training under a master scholar. Here are Adam Smith's thoughts on the matter...
Most of the time the job that a Data Scientist does is a job that a 20$/hour graduate worker does. The Big Picture, I think is how to orient a 30$/hour grad. to cooperate with a highly skilled Data Scientist.
Reminds me a job I had early on in life working under several intelligent people but unknowningly replacing 3.
Out of the 3 the 1st pushed me and encouraged me to move on when we debated who should stay, the 2nd was a surprise because I asked her why she was training me but sadly it was then I learned of her animosity toward me and her 30 years on payroll.
Which brings me to the last person I was replacing - the data scientist, the man that verfied my numbers and surely was verifying a lot of other things.
so having the guilt of the first two I happened to have a situation outside of my control land in my lap, I left the company immediately, positive they kept him onboard for atleast another 6 months to a year.
great experience nonetheless.
tldr: cost reduction hire, had to be vetted 1st - split before my conscience weighed too heavy.
So Western centric. Now imagine you're a brilliant 2x year old guy in Ukraine, Belarus or Vietnam. That $30/hour would put you in the upper 1% of income in your country.
Genuinely curious about this - why do developers who are from those country not charge more?
Why is their work deemed $30/hr where as a Western counterpart could get away with charging $90? Same goes for people in India as well - if they are genuinely good at what they do why do they not charge a higher rate?
Because customers aren't usually capable of assessing the technical capabilities. All they see is self-confidence, personal charm (English and accent is very important in this category) and past experience.
So what happens is that a guy with these qualities, along with enough technical knowledge to asses the skills, resells the work of cheap foreign developers for enormous profit, probably as a consulting company.
People with powerful personalities can get out of that, but those introverted or with some form of Asperger's won't. I know of several instances like that personally. They just accept that they're worth $20k/year as a fact, even though if they were more assertive and extraverted that could eventually turn into $200k.
If they try to start out on their own, they start with low confidence, which means low prices. Which leads to people thinking they aren't capable of anything complicated, so they get only shitty jobs. Which reinforces their low confidence in their skills and they don't try much.
Steve Jobs of the world will always earn more than Wozniaks.
Not everyone in those country can do work for other countries -- a majority of them have to do work for other business in their own country.
Which leads to the fact that you can hire a BS educated employee in those country for less than $200-300/months: if the programmer charge more, they're just gonna screw automation and hire more people instead.
As in all things, the answer is supply and demand.
The demand for these services mostly comes from the West, and there is a big advantage to local workers. So assuming there is a large supply of skilled data scientists in India and Ukraine, but not the demand, then they can't charge higher wages.
Furthermore, if these barriers could be broken down, and there was no longer an advantage to being local, then we would expect the price of Western and other workers to equalize, but the final wage would be somewhere in between the current Indian wage and the current Western wage.
I disagree with you based on my personal anecdotal evidence. Our Ukrainian was paid the same (gross) as the Americans. The Viets too. There was no noticeable discount based on location.
You get a discount for age (younger = cheaper; flattens out after 5-10 years), experience (probably why age is discounted), and things that are not politically correct to talk about but are very real effects affecting the labour market.
Maybe my experience was biased by hiring for relatively specialist skills (Haskell, dev ops) which may make for more meritocratic markets.
Probably the only developers that your company would hire are those that managed to market themselves as equal to Western ones, which are definitely far from average freelancers.
Also, due to enormous cost of living and tax differences (~0 income tax in Ukraine) the same gross pay resulted in 2-3x times higher disposable income, so effectively the Americans were relatively underpaid, which is kind of funny.
Or indicative of the crappy job market in the US for those specialist skills.
The thing is, there's no such thing as an "average" developer. Every company and its tech stack has a unique set of conditions that will match better with some people than others. If your codebase is bog standard [insert popular framework], someone who has done it 4 times before, but couldn't make a relational schema because he doesn't know what an SQL constraint is and thinks functional programming is "academic ivory tower stuff" is going to be a lot more value adding upfront than someone with a PhD in category theory and 5 years AI experience; but the latter is the person you want building your search algorithm or trading technology.
And then, as with academia, you have non-financial compensation; for example, we got a lot of people interested by offering Haskell jobs which are very rare today (at least in stable places); remote work was another plus, as many people have a comfortable situation and don't want to uproot their life for the sake of a new opportunity, and it allows people to arbitrage cost of living differences between cities. But Scala didn't work out for LinkedIn, which is rewriting all of it back to Java, and remote work didn't work out for Yahoo, so YMMV.
Someone gets paid less than me for the same job: "Beware, no way he can charge that little and still do a good job!".
Someone gets paid more than me for the same job: "He must be spending more time on politics than actually working, I would earn as much if it weren't for my high standards!".
The MOOCians are coming. This is why I focused on other predictive modeling areas instead of just basic ML algos as well as more engineering intensive activities.
32 comments
[ 3.6 ms ] story [ 90.1 ms ] threadIn my case, I was interested in machine learning, and after doing some internships as an undergrad concluded that there was no easy path to work on interesting ML problems in industry with only a bachelor's degree (both because the positions weren't available, and because I didn't know enough to do the work). So for me the first-order benefit is that I get to spend my time learning about and working on stuff that I'm interested in, and developing a skill set and a credential that should let me keep working on interesting things for the foreseeable future. It's different for other people, of course: some want to become professors, some want knowledge for knowledge's sake, some could pursue their chosen area equally well in industry but have a specific crazy idea they want the freedom to explore (I think this is more common in systems research), some want the lifestyle flexibility to go skiing in the middle of the week and make up by cramming work on weekends, etc. For most people it's really a mix of these factors and others.
The other main bit of grad school advice I have is that your relationship with your advisor is extremely important. The chance to be mentored one-on-one for six years by an extremely bright person you click with, share interests with, and who wants you to succeed is incredibly valuable. On the flip side, the possibility of being stuck for six years under a boss who doesn't care about you or know how to motivate you and develop your skills can be a massive liability (and one that's harder to escape than the equivalent situation in industry, since a lot of people internalize the value structure of academia and think of quitting a PhD as a failure on the part of the student rather than the advisor). So whatever you can find out about your likely advisors -- both by talking directly to them when you visit (are they looking for new students? do they seem enthusiastic about you specifically? how do they help their students find research topics? what is their philosophy of how to develop a student's skills as an independent researcher?), and by talking to their students: take them out to coffee and ask pointed questions like "is X a good advisor?", "what do you like/dislike about working with X?", "what sort of person is best suited to working with X?", "would you make the same choice again?" -- should have a huge bearing on your decision whether to attend, even if your program doesn't actually make you choose an advisor initially.
It's in no way the sole predictor, but the probability of someone have done hard things and being able to solve tougher problems [1] quicker is higher with a PhD than someone who spent the same time in industry. It's also tremendously competitive (in the same way, say, Goldman Sachs is in "business") so you can trust the PhD admissions committee to have done some filtering for you. Finally, it takes a lot of willpower to stick through it til the end (particularly in the US with its much longer programs), and drive is a strong predictor of adding value to a company later.
On the downside, few people in business realise that because they haven't hung around PhDs or thought about it much, and just see a "lack of industry experience", an "attitude" and "not the most commercial mindset" (usually with a mention of the "ivory tower").
A good corporate job will have you learn a lot (broader, less specialist, probably less hard); but most corporate jobs are not good, and pay well to compensate the drop in intellectual challenge and loss of skills that results vs your undergraduate self (although you'll pick up more useful soft skills, to an extent, vs learning to navigate bureaucracies which are the soft skills a PhD teaches you). I heard that across practically all academic fields.
[1] Unfortunately most of the problems people actually need solved in tech companies, like building a data warehouse, some reporting, a user friendly front end, are not "tough problems" in the way, say, building a machine vision engine tailored to your specific industry might be.
A grad assistant is usually not perceived as an employer by most universities, US and abroad. As such, from the university's perspective (which you took in your comment), one cannot say that they are being compensated, at all.
Assuming that you do understand that a graduate assistant (in this case, e.g. a research assistant who is also a student) is actually an employee of the university,
- The training they receive (courses et al) is what we call unpaid training. They do not receive salary for the hours they spend receiving training, which is a requirement for them to do their job (research assistant) efficiently and successfully.
- Would you accept payment in prestige if you were in, say, Google or Apple? A graduate school has much less prestige (hence the need for "internships").
- Independence does not exist for a grad assistant, whose not only livelihood but also school registration depends on her/his relationship with her/his "mentor" (i.e. boss / advisor to boss) and other department members. Not only job security but also school security depends on maintaining "good relationships" with these people. (As a student, on the other hand, s/he is expected to produce and engage in independent thought.) Additionally, this particular kind of employee cannot switch jobs easily since most jobs will not provide benefits for external education.
- No or lower-end benefits etc.
You can very easily hire a grad student on a "free market"[1] for even $15k/yr if they are [made to be] desperate enough. Most already are.
[1] Please read Adam Smith to understand how a "free market" actually operates. It's not what you think it is.
Grad students tend to be intelligent capable people (from privileged backgrounds too!). Therefore we don't expect grad students to be desparate, because we infer that they ahve a lot of other options.
The only sense in which grad students are desperate, is that they really want the other advantages that being a grad student confers, most of all the education and experience, and the degree they get at the end. And this was precisely the point of the post you replied to: graduate students are getting non-monetary compensation.
If you disagree, I challenge you to find one testimonial from a grad student whose story is that they were so desperate for money, that they had no choice but to enroll in grad school to make ends meet.
https://en.wikipedia.org/wiki/Apprenticeship#Analogs_at_univ...
http://www.opm.gov/policy-data-oversight/pay-leave/salaries-...
Out of the 3 the 1st pushed me and encouraged me to move on when we debated who should stay, the 2nd was a surprise because I asked her why she was training me but sadly it was then I learned of her animosity toward me and her 30 years on payroll.
Which brings me to the last person I was replacing - the data scientist, the man that verfied my numbers and surely was verifying a lot of other things.
so having the guilt of the first two I happened to have a situation outside of my control land in my lap, I left the company immediately, positive they kept him onboard for atleast another 6 months to a year.
great experience nonetheless.
tldr: cost reduction hire, had to be vetted 1st - split before my conscience weighed too heavy.
Why is their work deemed $30/hr where as a Western counterpart could get away with charging $90? Same goes for people in India as well - if they are genuinely good at what they do why do they not charge a higher rate?
If they try to start out on their own, they start with low confidence, which means low prices. Which leads to people thinking they aren't capable of anything complicated, so they get only shitty jobs. Which reinforces their low confidence in their skills and they don't try much.
Steve Jobs of the world will always earn more than Wozniaks.
Which leads to the fact that you can hire a BS educated employee in those country for less than $200-300/months: if the programmer charge more, they're just gonna screw automation and hire more people instead.
The demand for these services mostly comes from the West, and there is a big advantage to local workers. So assuming there is a large supply of skilled data scientists in India and Ukraine, but not the demand, then they can't charge higher wages.
Furthermore, if these barriers could be broken down, and there was no longer an advantage to being local, then we would expect the price of Western and other workers to equalize, but the final wage would be somewhere in between the current Indian wage and the current Western wage.
You get a discount for age (younger = cheaper; flattens out after 5-10 years), experience (probably why age is discounted), and things that are not politically correct to talk about but are very real effects affecting the labour market.
Maybe my experience was biased by hiring for relatively specialist skills (Haskell, dev ops) which may make for more meritocratic markets.
Also, due to enormous cost of living and tax differences (~0 income tax in Ukraine) the same gross pay resulted in 2-3x times higher disposable income, so effectively the Americans were relatively underpaid, which is kind of funny.
The thing is, there's no such thing as an "average" developer. Every company and its tech stack has a unique set of conditions that will match better with some people than others. If your codebase is bog standard [insert popular framework], someone who has done it 4 times before, but couldn't make a relational schema because he doesn't know what an SQL constraint is and thinks functional programming is "academic ivory tower stuff" is going to be a lot more value adding upfront than someone with a PhD in category theory and 5 years AI experience; but the latter is the person you want building your search algorithm or trading technology.
And then, as with academia, you have non-financial compensation; for example, we got a lot of people interested by offering Haskell jobs which are very rare today (at least in stable places); remote work was another plus, as many people have a comfortable situation and don't want to uproot their life for the sake of a new opportunity, and it allows people to arbitrage cost of living differences between cities. But Scala didn't work out for LinkedIn, which is rewriting all of it back to Java, and remote work didn't work out for Yahoo, so YMMV.
Someone gets paid more than me for the same job: "He must be spending more time on politics than actually working, I would earn as much if it weren't for my high standards!".