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The data science field has been flooded with PhDs with nowhere else to go that have no background in engineering, and sadly often have a very poor understanding

The data science field has been flooded with PhDs with nowhere else to go that have no background in engineering, and sadly often have a very poor understanding of both machine learning and statistics. Companies were in a rush hire "data scientists" and boot camps like Insight were more than happy to pump out very impressive PhDs with just enough understanding to build a Keras model. I've worked in industry awhile doing DS work and have been astounded at the number of PhDs that both don't know how to write Python that doesn't live in a notebook and throw away years of disciplined experimentation experiences to just throw keras models at data until the needle moves. There do exist excellent data scientists out there, who are both very solid software engineers and really know their stuff mathematically, but I've found most of these people can't reliably find jobs because the people interviewing them know so little that good data scientists will be penalized for answer a stock question correctly. The field has been so flooded with amateurs that have no idea what they're doing, that potential mentors have been driven out, and now it's just a mess. To get a job doing DS if you do know what you are doing you have to play a weird game where you guess the incorrect answer the interviewer has in mind.

fock 6y ago
I do an introductory Python lab course at my university. It's targeted at engineers who still create graphs from Excel and then normally level up to MATLAB, if things get complicated (think insets, ...). I guess about 30% of the people previously did at least some of the YT/Udemy "courses" on datascience. It's really horrifying for me (not being an engineer myself, but imo having a relatively engineering-like mindset) to see these people horrified at simple tasks like writing a variadic function. "What do I need this for?". Well, it's using the programming environment. And then let them code up a simple version of Levenberg-Marquardt. The level of "why do I need to do this" is astonishing again...
- nitrogen 6y ago
  why do I need to do this IMO this is the number one problem of our modern culture around education. Popular culture makes it popular to treat education as pointless, and this even affects students who are pursuing difficult degrees. "Why do I need to study humanities? Why should I learn to code if I think I am born to be someone else's boss?" On the other hand, many teachers in K12 and early university have no ability to connect the "what" with the "why." "The curriculum is the curriculum. The test is the test." If we can solve these problems, our societies will be much better off.
  - wickedsickeune 6y ago
    If the educator cannot explain why the knowledge is useful, then he is unfit to teach it.
boterham 6y ago
Do you have any suggestions for where to start looking for good places to apply that don't suffer from this?
- baron_harkonnen 6y ago
  I personally have given up and turned mercenary. Even if you're passionate about statistics, machine learning, or any ds related discipline don't think of work as being a bigger part of your identity than the average star bucks barista does. Find a team/company that pays you well and isn't too opinionated, with low ego (if possible). Don't look for challenging work, the few places it exists already have the people they need, whereas the companies that pretend they have challenging problems tend to be insufferable. Look for a team where you can check-in and check-out without too much stress and get paid well.
nerdponx 6y ago
Not to mention the dark pattern of giving data scientist candidates an unsolved industry problem as their interview take-home task, and then telling them to only spend 4 hours on it. Data science hiring often feels like a competition where the winner is the one who has the most free time and willingness to do other people's work without compensation. It's kind of a fucked up field right now.
theflyinghorse 6y ago
I work at a place with a very high count of PhDs. Some of them write code. All of them view writing code as something menial and unimportant and its shows in the resulting work, which from my experience is atrocious. Of course I understand that YMV, but I will forever be skeptical of anyone writing code with a PhD after working here.
- lordgrenville 6y ago
  Are they CS/EE PhDs?
  - theflyinghorse 6y ago
    The ones who's work I've seen are EE
  - baron_harkonnen 6y ago
    Think back to your own CS professors, were any of them particular good software engineers? I've found that Physics PhDs tend to have the highest probability of being good coders since a certain subset of them get bit by the software bug when they need to write non-trivial amounts of code to solve research problems.
    - analog31 6y ago
      I got my physics PhD in the early 90s. Physics has had a tradition of interest in programming that goes back decades. We've always had "big data," meaning big relative to the tools available at any given moment. We ran out of problems that could be solved by pencil and paper in the 1930s. Every physics student at my college had to take FORTRAN, plus programming was assumed in many of the other courses, and we also took an electronics course that included digital techniques. And maybe the main thing was simply that programming was interesting and fun. We've also had a tradition of learning to do everything ourselves, for better or worse. I had no access to a professional programmer.
jhap 6y ago
> The data science field has been flooded with PhDs with nowhere else to go that have no background in engineering, and sadly often have a very poor understanding of both machine learning and statistics. I am a PhD student in a non-engineering field. I've been taking as many math and stats courses as I can, but what other courses should I be trying to take if I want to excel as a data scientist? Software engineering CS type courses?
- baron_harkonnen 6y ago
  My question is: "Why are you pursuing a PhD if you want to end up as a data scientist?" I've known a surprisingly large number of people that are mid-phd thinking about data science as a career. Don't pursue 5+ years of learning to master the world of academic research if your goal is to help people sell t-shirts or whatever. Certainly there are some people pursuing specific PhDs, such as those in computer vision and nlp where there are some industry options that might offer more challenging/interesting research than academia. It makes sense if you're a PhD at NYU or Stanford in CS fields related to neural networks to go work for Yann Lecun at Facebook or Geoffrey Hinton at Google. But if you're, say a biologist that wants to sell clothes online... why spend 6 years working in academia to do that? Is your dream really to optimize clothing sales? If so don't be a biologist. If your dream is biology, why in the world would you set your course on selling clothes? I get it if your dream is biology but you can't find a tenure track job and so you pivot to industry... but if you are mid-phd, what are you doing there? If you love your subject, try to find a way to work in that and if you don't, don't waste your time. Data Science is not a glamorous job, and the vast majority of companies it is literally bullshit. The people solving mind-bendingly hard problems are already in programs specializing in those problems because that's what they are passionate about. On top of that DS is way over indexed at most companies. If you're mid-phd now I would expect a serious contraction in DS jobs in the next 5 years. DS will be a niche job after the next market "correction"
  - zwaps 6y ago
    I mean you have to think about cause and effect here. DS will contract because many/some data scientists simply aren't good enough, and most DS just doesn't do what it is supposed too. First, like you said, there are the stray PhDs who do it since they know research and some statistical applications. Second, there are hordes and hordes of DS people who "learned" their skill with some bootcamps or online courses, which means they know enough to write notebooks and glue together functions. Their understanding of theory is often shallow. In either case, it is hard to "blame" someone for taking an attractive job. But it isn't good for the discipline. The appeal of DS is clear for companies. But the problems it promises to solve are much more complex than we collectively recognize - or are willing to admit. In my opinion, doing causal inference is a difficult, unsolved, and deep topic and no single course would equip to you to tackle it. It takes domain knowledge and multiple years of stats/math/ML (all of them, not one of them). And yet, causal inference is what 90% of people want ML to be. A model that works on some dataset is not a model that is useful in light of the true latent DGP. Yet, when we want to sell T-Shirts, what do we really want? Hence, when I look at the problems that ML is supposed to solve, I think that most people calling themselves DS on linkedin are not really equipped for it. And there is a case to be made that some fields where PhD researchers train to solve such causal inquiries indeed are better equipped to tackle the issue. For example, if it's about selling shirts, I would take an econometrician with some data engineering skills over a coursera superstar any day of the week. I think if you do a PhD in ML/Stats/Biostats/Econometrics/etc., it is reasonable to pursue a career in DS. It's what statistics _is_ now. If you have some other PhD and know some Anova, OLS and Stata - or if you have CS background but know some Jupyter and Keras - then it's essentially career change. It might work, but probably not without a hitch. So I agree with you, but I'd reframe it: It's unclear to me whether we need a contraction, or whether we instead need a quality update. I disagree with you in one point: I do not think we will make progress in DS (getting it to work in more use cases) by treating it like a solved problem, a skill like milling that needs talent and experience, but not academic education. If we do that, I think DS will contract because it will stagnate in usefulness. My point here is not to accuse anyone of being a bad DS. I am sure there are many ways to become efficient. But even the theory of causal inference with simple linear models goes far, far beyond what I saw in ML hiring tests, online courses and so forth. And solving the problems it tackles is not accomplished by throwing more layers at it. For other ML algos, we aren't even close yet at understanding these issues on a similar level. In the end, what we need are actual ML scientists. They should neither be pure statisticians, nor pure subject-matter experts, nor pure computer scientists - as we mostly have now. We also need more than the current ML programs that are mostly clobbered together from other areas. For example, people who publish in ML research are probably very useful in a company that has to deal with that exact problem. Any scientist knows, of course, that even a fairly adjacent question may already require tons of different knowledge. DS is, will remain, and probably should be an academic field, because there are more open than solved problems right now.

fock 6y ago

I do an introductory Python lab course at my university. It's targeted at engineers who still create graphs from Excel and then normally level up to MATLAB, if things get complicated (think insets, ...). I guess about 30% of the people previously did at least some of the YT/Udemy "courses" on datascience. It's really horrifying for me (not being an engineer myself, but imo having a relatively engineering-like mindset) to see these people horrified at simple tasks like writing a variadic function. "What do I need this for?". Well, it's using the programming environment. And then let them code up a simple version of Levenberg-Marquardt. The level of "why do I need to do this" is astonishing again...

nitrogen 6y ago
why do I need to do this IMO this is the number one problem of our modern culture around education. Popular culture makes it popular to treat education as pointless, and this even affects students who are pursuing difficult degrees. "Why do I need to study humanities? Why should I learn to code if I think I am born to be someone else's boss?" On the other hand, many teachers in K12 and early university have no ability to connect the "what" with the "why." "The curriculum is the curriculum. The test is the test." If we can solve these problems, our societies will be much better off.
- wickedsickeune 6y ago
  If the educator cannot explain why the knowledge is useful, then he is unfit to teach it.

boterham 6y ago

Do you have any suggestions for where to start looking for good places to apply that don't suffer from this?

baron_harkonnen 6y ago
I personally have given up and turned mercenary. Even if you're passionate about statistics, machine learning, or any ds related discipline don't think of work as being a bigger part of your identity than the average star bucks barista does. Find a team/company that pays you well and isn't too opinionated, with low ego (if possible). Don't look for challenging work, the few places it exists already have the people they need, whereas the companies that pretend they have challenging problems tend to be insufferable. Look for a team where you can check-in and check-out without too much stress and get paid well.

nerdponx 6y ago

Not to mention the dark pattern of giving data scientist candidates an unsolved industry problem as their interview take-home task, and then telling them to only spend 4 hours on it. Data science hiring often feels like a competition where the winner is the one who has the most free time and willingness to do other people's work without compensation. It's kind of a fucked up field right now.

theflyinghorse 6y ago

I work at a place with a very high count of PhDs. Some of them write code. All of them view writing code as something menial and unimportant and its shows in the resulting work, which from my experience is atrocious. Of course I understand that YMV, but I will forever be skeptical of anyone writing code with a PhD after working here.

lordgrenville 6y ago
Are they CS/EE PhDs?
- theflyinghorse 6y ago
  The ones who's work I've seen are EE
- baron_harkonnen 6y ago
  Think back to your own CS professors, were any of them particular good software engineers? I've found that Physics PhDs tend to have the highest probability of being good coders since a certain subset of them get bit by the software bug when they need to write non-trivial amounts of code to solve research problems.
  - analog31 6y ago
    I got my physics PhD in the early 90s. Physics has had a tradition of interest in programming that goes back decades. We've always had "big data," meaning big relative to the tools available at any given moment. We ran out of problems that could be solved by pencil and paper in the 1930s. Every physics student at my college had to take FORTRAN, plus programming was assumed in many of the other courses, and we also took an electronics course that included digital techniques. And maybe the main thing was simply that programming was interesting and fun. We've also had a tradition of learning to do everything ourselves, for better or worse. I had no access to a professional programmer.

jhap 6y ago

> The data science field has been flooded with PhDs with nowhere else to go that have no background in engineering, and sadly often have a very poor understanding of both machine learning and statistics. I am a PhD student in a non-engineering field. I've been taking as many math and stats courses as I can, but what other courses should I be trying to take if I want to excel as a data scientist? Software engineering CS type courses?

baron_harkonnen 6y ago
My question is: "Why are you pursuing a PhD if you want to end up as a data scientist?" I've known a surprisingly large number of people that are mid-phd thinking about data science as a career. Don't pursue 5+ years of learning to master the world of academic research if your goal is to help people sell t-shirts or whatever. Certainly there are some people pursuing specific PhDs, such as those in computer vision and nlp where there are some industry options that might offer more challenging/interesting research than academia. It makes sense if you're a PhD at NYU or Stanford in CS fields related to neural networks to go work for Yann Lecun at Facebook or Geoffrey Hinton at Google. But if you're, say a biologist that wants to sell clothes online... why spend 6 years working in academia to do that? Is your dream really to optimize clothing sales? If so don't be a biologist. If your dream is biology, why in the world would you set your course on selling clothes? I get it if your dream is biology but you can't find a tenure track job and so you pivot to industry... but if you are mid-phd, what are you doing there? If you love your subject, try to find a way to work in that and if you don't, don't waste your time. Data Science is not a glamorous job, and the vast majority of companies it is literally bullshit. The people solving mind-bendingly hard problems are already in programs specializing in those problems because that's what they are passionate about. On top of that DS is way over indexed at most companies. If you're mid-phd now I would expect a serious contraction in DS jobs in the next 5 years. DS will be a niche job after the next market "correction"
- zwaps 6y ago
  I mean you have to think about cause and effect here. DS will contract because many/some data scientists simply aren't good enough, and most DS just doesn't do what it is supposed too. First, like you said, there are the stray PhDs who do it since they know research and some statistical applications. Second, there are hordes and hordes of DS people who "learned" their skill with some bootcamps or online courses, which means they know enough to write notebooks and glue together functions. Their understanding of theory is often shallow. In either case, it is hard to "blame" someone for taking an attractive job. But it isn't good for the discipline. The appeal of DS is clear for companies. But the problems it promises to solve are much more complex than we collectively recognize - or are willing to admit. In my opinion, doing causal inference is a difficult, unsolved, and deep topic and no single course would equip to you to tackle it. It takes domain knowledge and multiple years of stats/math/ML (all of them, not one of them). And yet, causal inference is what 90% of people want ML to be. A model that works on some dataset is not a model that is useful in light of the true latent DGP. Yet, when we want to sell T-Shirts, what do we really want? Hence, when I look at the problems that ML is supposed to solve, I think that most people calling themselves DS on linkedin are not really equipped for it. And there is a case to be made that some fields where PhD researchers train to solve such causal inquiries indeed are better equipped to tackle the issue. For example, if it's about selling shirts, I would take an econometrician with some data engineering skills over a coursera superstar any day of the week. I think if you do a PhD in ML/Stats/Biostats/Econometrics/etc., it is reasonable to pursue a career in DS. It's what statistics _is_ now. If you have some other PhD and know some Anova, OLS and Stata - or if you have CS background but know some Jupyter and Keras - then it's essentially career change. It might work, but probably not without a hitch. So I agree with you, but I'd reframe it: It's unclear to me whether we need a contraction, or whether we instead need a quality update. I disagree with you in one point: I do not think we will make progress in DS (getting it to work in more use cases) by treating it like a solved problem, a skill like milling that needs talent and experience, but not academic education. If we do that, I think DS will contract because it will stagnate in usefulness. My point here is not to accuse anyone of being a bad DS. I am sure there are many ways to become efficient. But even the theory of causal inference with simple linear models goes far, far beyond what I saw in ML hiring tests, online courses and so forth. And solving the problems it tackles is not accomplished by throwing more layers at it. For other ML algos, we aren't even close yet at understanding these issues on a similar level. In the end, what we need are actual ML scientists. They should neither be pure statisticians, nor pure subject-matter experts, nor pure computer scientists - as we mostly have now. We also need more than the current ML programs that are mostly clobbered together from other areas. For example, people who publish in ML research are probably very useful in a company that has to deal with that exact problem. Any scientist knows, of course, that even a fairly adjacent question may already require tons of different knowledge. DS is, will remain, and probably should be an academic field, because there are more open than solved problems right now.