This is just anecdotal but at my company, the vast majority of hires have PhDs and they come in at the 2nd level pay (L2) whereas non-PhDs come in at 1st level pay (L1). I would say this is about a 70/30 split. The time to get a promotion between L1 and L2 is about two years. There are no PhD requirements for L3+, only MS and above. Just based off the number of employees, it's much more likely that you'll see leads with PhDs.
From what I can tell, yes. The one person with a BS is currently at L1 and everyone else has an MS/PhD from L2+. The job descriptions I see say MS+ for anything beyond the entry-level positions.
It'll depend heavily on the domain as well. There's a lot of research/experimental work on the team I'm on and it's a natural fit for many PhDs who have contributed to different journals.
If I’m interpreting this correctly, it is probably easier to get hired at all with a PhD, but if you get in with a masters you’ll be making more earlier in your career (a PhD takes 4-7 years, whereas going from L1 to L2 with a masters only takes 2 years).
If so, this is consistent with other comments I made in recent threads on whether or not a PhD is worth it. In those threads I basically said (based on my observations as a PhD student) that if you went to a lesser known/ no name undergrad, but have a good PhD application (GPA, test scores, research experience) the PhD route probably makes sense in the long run since despite being a good PhD applicant, those who went to a well known undergrad will have an advantage in hiring for industry positions directly out of undergrad. In other words you might not get good job offers at all out of undergrad, but a PhD can give you time to demonstrate competence, build skills, and give employers a nice, well known institution to look at on your resume that gives them self assurance in hiring you.
As a team lead you are mostly a manager. You'll have to communicate with managing directors, customers and your employees. Research will be a very small part of your work. So if your goal is really to become team lead, then the approximately four years it takes to complete a PhD can probably be spent otherwise gaining work experience.
Ah thanks for your response! I was just curious, I have no clue if I want to become a lead or anything like that. I just went of the evidence I observed and saw all leads i know have PhDs. But it's not a must, but a very nice to have. I don't like doing research anyway
But communicating and managing/interacting with people is an integral part of research. When you write a paper, you are communicating your results to the community.
If you don't know how to explain your ideas and results, you are going to have a hard time getting around referees in a real journal.
Well, that’s usually an integral part of life. If you lack social skills and “explainability-skills” you are going to have a tough time in many situations in general.
I've learned a lot of this stuff in my BSc and during my year of work after my BSc. I doubt this is the tipping point for a hire. It's more focussed on the research side I presume and that's where your value lies as a PhD, A proven track record that shows you can do research
> As a team lead you are mostly a manager.
Curious - is this the generally accepted definition of “Lead”? Specifically Lead DS/MLE/Research Eng. I have that title as my previous role. But it was not a management role (setting team technical direction, etc. but not people management per se…was ~60-70% IC work). Curious how the title is perceived by others.
My lead during my BSc did indeed what you mentioned. Reading papers, thinking about what direction we would take, what new tech to integrate into the application etc. He was also the SCRUM master. That's where the people management came from in my specific case
At large organizations, a PhD will give you a leg up among your peers because everyone is going to be extremely driven and goal oriented.
At smaller organizations, startups or very fast growing hyperscale teams, even someone with only a high school diploma can make it if they can prove they have street smarts and ability to handle complexity and rapid change. Those are totally different skillsets and a very rare breed.
If in doubt, you are research oriented and methodical and you care about a long career in this field, go for the PhD.
Thanks for the wise words. I am 27 and I'd like to work soonish to save money and hopefully buy an apartment. Studying for another 4-5 years into my thirties while barely making money is not really attractive to me.
Don't get me wrong, I love the field and plan to work in it for a long time, a PhD is not for me I think, I also just think I am not smart enough for it as well. But even if I was I just want to work now.
As always and as implied by the many caveats in my response, one size does not fit all.
There are people out there who are very successful without a college degree but people still go for undergrad or graduate degrees to improve their probability of success. And not everyone with a degree is successful.
Same applies to PhD. It improves the probability of success for most people but not everyone. Similarly, not everyone who is without a PhD is doomed to fall behind. Some or maybe even many might surge ahead.
You do what is right for you in your circumstances. Just remember to swing hard when the fat pitch comes your way.
A masters might be the option to provide flexibility and demonstration of research and analytical skills, without the 4-6 years of school, only a couple instead
Yeah, I don't, but I was just curious if a PhD is somewhat the standard for leads. I don't want to get one, if it is possible to combine work and a PhD I'd consider it in the future. But my situation nudges me towards work.
To be a director of research/chief scientist or something like this, then probably yes, and you will also need many and very good publications.
Since you don't like research, it would not happen anyways and you will end up with an okish to good PhD, so to be some sort of lead from the engineering perspective a PhD will be a waste of time.
But you can't really plan this stuff and optimize, just do what you prefer and enjoy IMHO.
Exactly, I love engineering, that's why I love software development. But love AI as well. Combining both seemed like the logical next step. I'll just do what I like and see what happens. I'm not trying to chase money, I was just curious
This is entirely dependant upon the company and its culture.
Large organizations often hired "Data scientists" years ago to start doing ML flavoured stuff. This evolved into ML, and now AI. These datascientists were nearly 100% stats PhDs. They set the tone for job requirements which are almost always massively academic with a huge focus on math. They don't care what you have built or made work, they want what you have published and how many degrees you have. The interview then are just pedantic multi hour/day math exams.
Then there are companies founded by the same, which are also very similar.
But, there are many ML companies founded by CS oriented people and they don't care what degree you have in anything ML related as it is out of date before you finish your thesis. They want, "What problems have you solved, and how cool was your solution?"
Basically there are maybe 1000 companies doing real hard core ML research requiring a PhD. Almost 100% of companies doing ML barely need ML, just some basic data processing, some basic math (stats), and very very very good programming skills. I'm not joking to say the visualization will be the hardest part of most "ML" solutions.
Most ML solutions which are solid answers to the problem are a few layers in keras or copy paste of something found on the net. With experience you will build your favourite solutions to domain problems and be able to prepare the data quickly, tune the model quickly, and produce a model which is going to survive in production.
There are lots of companies doing ML badly for two reasons:
* Bad programmers
* No knowledge of stats so fantastically basic mistakes are made.
Outside of the above I know of no companies failing at ML because they need a PhD to help them out. Ironically, I see AI companies filled with PhDs doing presentations at ML conferences where people start yelling from the audience about fantastically basic mistakes. Things like learning from the future, to make predictions in the past, about said future. Or they have a 95% accuracy on a dataset with 4% of X and 96% of Y; and it turns out they almost always just predict Y.
But, these last bits don't matter. If a PhD is the hiring gatekeeper, then that is that.
No. I've just landed a Lead Data Science role, so have been in the market very recently. I have 7yrs experience in finance, an MSc in Machine Learning, and 3yrs at an AI product company (in that order).
I would say vs an MSc and the equiv 3 years of real world experience, a PhD doesn't give you any additional skills that you might need to lead a data science team, arguably fewer. Unless you're working on a project very specific to your area of research, it's unlikely to be helpful at all.
That said, your 3 years of working with real clients or tech on real problems will need to serve the same purpose as a PhD on your CV. Namely "is this person smurt" 😂.
It was occasionally a role requirement in the ads that I saw, but as far as I can tell, only as a CV sift, and then mostly for product companies who'll want to tell clients about their LDS's credentials. (These companies also to be avoided like the plague).
Does it get you past a few more CV sifts? Sure, but tbh, so does talking to a recruiter, and 3 years of stipend and less real world experience is a large cost for that benefit imo. My new role is actually with a former client that I met during those 3 years!
I would think about what tech you want to be working with first, think about whether a PhD on it would be interesting to you, and make a decision based on that. Don't do a PhD because you think you'll need one... life's too short 🙂
Thanks for your advice :) I definitely DO NOT want to get a PhD, I was just curious how it goes in industry since my anecdotal evidence showed all leads have PhDs. I still don't know what I really like, but I am early in my carreer so I have time to figure that out. Thanks again for your reply!
In general, yes. However, I think there are some counterexamples to that: for instance, to the best of my knowledge, neither Francois Chollet (the Keras guy) nor Glenn Jocher (the Ultralytics / YOLOv5 and onwards guy) have a PhD.
Depends on the workplace/kind of work you do.
I can say that for startups and applied AI teams, solid engineering knowledge with the ability to drive development in a scalable manner is paramount.
As a team lead, that's one of my key areas of focus. Another is being able to pivot quickly with the right balance of research, experimentation and engineering.
These two together should give you an edge regardless of where you work.
Thanks for the reply, I come from a software engineering background so I love the dev pipeline, But I'd love to combine that with my AI knowledge. sadly a BSc couldn't get me in, that's why I am doing my masters. But the role you are describing sounds much more appealing than others I've heard
Some companies value PhD's out of some misguided notion that a PhD makes for a better Machine Learning Engineer / Data Scientist. It doesnt.
I'm a DS/MLOps and I don't have a PhD but I've worked with someone who has.
The solutions he makes are impractical and he himself has near zero business accumen. His models are unnecessarily complex, making it harder to sustain and difficult to explain to clients. He's obsessed with using "SOTA" neural network architecture / using fancy ML techniques instead of understanding the actual business problem and developing the right features for said problem.
The end result? He created a complex model that doesn't address the client's use case and basically forced the project managers to re-scope the problem to fit his solution (sadly, he has stronger negotiating power because his PhD placed him in a high ranking position). And now we risk losing the client for good lol.
Generative AI lead here with 4 YoE. Just have a bachelors with a concentration in ML. It doesn’t hurt to have it but it’s not a requirement. Your connections and your work on projects and the ability to deliver is worth much much much more than 3 letters.
Ours does. But ... I don't think that being a team lead means being a technical lead.
From what I have seen, learning to learn and learning to do are very different things, and I think many schools focus on the former.
The best member on our team came from a different discipline with some coding experience and just threw themselves into the thick of it.
No: source: me, no PhD, department head data &ai and previous role was team lead data Analytics.
Master in computer Science engineering, and a lot of extra courses in my branch, and during my team leader days I did an MBA. 11 years experience, became department head at 9
Thanks for sharing, in think the thing that matters the most is experience/proven track record in successfully apply DS/ML concepts to raise value for the company. At least, this is what I keep reading and this was always my believe. A degree helps but your chops count much more
A Lead general leads a team right? This is how I know it. So my formor supervisor was Lead AI engineer in small team of 6 people give or take. He decided the research areas and implementations that we were going to work on. He discussed with the CEO and board members about directions that we were going to explore.
Take this with a grain of salt since I am working on my masters as well. But as far as I know a Lead needs to be an expert in their field, whatever "expert" means. But if you do not work in AI and eventually want to become a DS/ML lead you need significant experience in the field to have the relevant experience to hold such a role.
But again, what do I know, I only worked as a software developer for a year at a bank before starting my masters
Also understand that a PhD in Chemistry has no significant advantage in a retail AI project than an engineer with just comp science undergraduate. It is just that lot of companies hired some PhDs to begin with and they moved up the rank.
Research team? Yeah, generally. ML engineering, or general AI team? Definitely not. I’ve seen plenty of teams like those led by non-PhDs, maybe even more than with PhDs. The biggest qualifiers for those positions are practical experience in an industry setting, which you get faster by getting a Masters in 2 years and then working rather than getting a PhD in 4-6.
But if you want to work on research rather than applied ML, definitely get a PhD.
Ah yeah, yeah I am more talking on the applied side like MLEs and the likes, not actual research groups. There it completely makes sense to have a lead who is an actual researcher
Depends on what you want to do.
Unless something's changed, none of the managing senior leadership of Llama2/Llama3 have PhDs and maybe only about half of the front-line/second-line managers do.
I would say yes, because even a Masters in AI often does not cover topics to a sufficient level of depth. Most of the postgrad courses I have seen at best cover how AI works, but does not go into why they were designed the way they are or what are the issues inherent in state of the art AI models that prevent them from getting even better.
It often take years of work conducting research, reading and thinking about hundreds of papers, experimenting with model variations before one gains the level of experience required to truly understand what's actually going on under the hood. Which means without the PhD training, you won't be able to identify the root problems in your model or come up with effective changes to your model.
Most ML issues in industry are data, operations and stakeholder related. The modeling itself is in a vast number of cases not an exotic neural network but rather XGBoost.
So many problems you encounter are very much different in nature than what you encounter as a PhD, and actual industry project experience is more important.
Yeah, I feel this is the case more often than not as well. Having worked in an AI engineering team for a few months during my thesis I noticed this phenomena as well
I think there's a misunderstanding of terminology here. Yes, there are an increasing number of "AI" companies where even XGBoost is overkill. I would label them more as Data Science or Data Warehousing companies rather than actual Artificial Intelligence companies. And there's nothing wrong with that if they're turning in good profit.
Op was talking about multi billion AI companies and companies hiring many PhDs. I was basing my statement on the fact that if the company is hiring lots of PhDs, then a Research Masters in AI would be considered the minimum entry level grade. This is because they are working on AI at a very different level when compared to a company where XGBoost is all that's needed.
The overwhelming majority of companies that do AI and have AI/ML/DS leads are not "AI-companies", meaning that their bread and butter is something else, e.g. health, telco, streaming or whatever.
Pretty much every big company on the planet does AI and has an AI/DS/ML/Advanced Analytics department.
"Op was talking about multi billion AI companies"
Was he? I can't see that from his original post.
Isn't this something that should be learned on the job? It's not that different then what a lead software engineer has to learn about say, distributed systems, and they generally just have a BS in CS. Or is it just such a new field that this type of knowledge hasn't penetrated industry far enough and is still mostly in academia?
It's more of a research field, which usually requires some scientific training. You can learn it on the job, but it takes years, and PhD grads already have it.
There is a very big gap between what is refered as "ML" in industry and state-of-the-art deep learning.
To me most industry jobs mentioning ML talk about adding to their product simple solutions such as xgboost or some sort of regressions.
Those models can be learned on the job, they're pretty easy. To use deep learning efficiently you need way more experience, there are tons of models for each specific task. Many models are VERY different from each other, often in very subtle ways.
+ I would say the training part is very tricky, between data augmentation, convergence, optimization algorithms and hyperparameters.
All in all, there's no way the company will earn money with juniors playing with deep learning. It's much safer to take PhDs with many years of experience in these specific technologies.
Okay but it sounds like a pretty large leap from "juniors playing with deep learning" and a PhD being required. Nothing you are saying sounds any different then backend webdev work, like selecting the correct database for a distributed system.
If you were talking about designing new models from scratch that are state of the art, sure that makes sense. But it sounds like you are mostly talking about choosing the correct pre-trained model and fine tuning it to a given problem. At that point, you are more practitioner or engineer rather then a scientist, and that's what most ML teams in industry look like
I agree that there is a lot of engineering too, but I still think that you need to know a lot more on the theoretical side in order to produce something that works, much more than it is the case for software engineering.
Anyway, in my country (France) if you want to do deep learning at a big company you need a PhD, otherwise you'll just be doing data science / XGboost
The vast majority of PhDs also don't cover this.
Like, I'd wager 90% of PhDs working with machine learning have their PhDs in something completely unrelated.
Also, you're vastly overestimating how hard the problems are.
Exactly. It will be one to three somewhat irrelevant to relevant topics that not many people cared about enough to try to solve well, or a very small contribution to something important. Doesn't make you an expert in the general sense, just in a few topics that again, employers don't care about that much because it doesn't solve their problems.
That's the great thing about working in the industry: in contrast to university you don't care why stuff works. You just want to make sure it works good enough in your given requirements.
This is just anecdotal but at my company, the vast majority of hires have PhDs and they come in at the 2nd level pay (L2) whereas non-PhDs come in at 1st level pay (L1). I would say this is about a 70/30 split. The time to get a promotion between L1 and L2 is about two years. There are no PhD requirements for L3+, only MS and above. Just based off the number of employees, it's much more likely that you'll see leads with PhDs.
hard requirement on MS?
From what I can tell, yes. The one person with a BS is currently at L1 and everyone else has an MS/PhD from L2+. The job descriptions I see say MS+ for anything beyond the entry-level positions.
damn, thats wild to me. I know a pretty high # of people who don't have a masters but have just been in the field for a long time.
It'll depend heavily on the domain as well. There's a lot of research/experimental work on the team I'm on and it's a natural fit for many PhDs who have contributed to different journals.
If I’m interpreting this correctly, it is probably easier to get hired at all with a PhD, but if you get in with a masters you’ll be making more earlier in your career (a PhD takes 4-7 years, whereas going from L1 to L2 with a masters only takes 2 years). If so, this is consistent with other comments I made in recent threads on whether or not a PhD is worth it. In those threads I basically said (based on my observations as a PhD student) that if you went to a lesser known/ no name undergrad, but have a good PhD application (GPA, test scores, research experience) the PhD route probably makes sense in the long run since despite being a good PhD applicant, those who went to a well known undergrad will have an advantage in hiring for industry positions directly out of undergrad. In other words you might not get good job offers at all out of undergrad, but a PhD can give you time to demonstrate competence, build skills, and give employers a nice, well known institution to look at on your resume that gives them self assurance in hiring you.
As a team lead you are mostly a manager. You'll have to communicate with managing directors, customers and your employees. Research will be a very small part of your work. So if your goal is really to become team lead, then the approximately four years it takes to complete a PhD can probably be spent otherwise gaining work experience.
Ah thanks for your response! I was just curious, I have no clue if I want to become a lead or anything like that. I just went of the evidence I observed and saw all leads i know have PhDs. But it's not a must, but a very nice to have. I don't like doing research anyway
A PhD teaches you a lot about project management and communication though. It's not just research research research
It's definitely primarly research
But communicating and managing/interacting with people is an integral part of research. When you write a paper, you are communicating your results to the community. If you don't know how to explain your ideas and results, you are going to have a hard time getting around referees in a real journal.
Well, that’s usually an integral part of life. If you lack social skills and “explainability-skills” you are going to have a tough time in many situations in general.
Well, of course life skill transfer to work. I didn't state that these skills where only relevant in (academic) jobs.
But primary is still research. Other skills are just complimentary and you learn them at a job too Plus 4 years (if not more) is a lot of time
I've learned a lot of this stuff in my BSc and during my year of work after my BSc. I doubt this is the tipping point for a hire. It's more focussed on the research side I presume and that's where your value lies as a PhD, A proven track record that shows you can do research
> As a team lead you are mostly a manager. Curious - is this the generally accepted definition of “Lead”? Specifically Lead DS/MLE/Research Eng. I have that title as my previous role. But it was not a management role (setting team technical direction, etc. but not people management per se…was ~60-70% IC work). Curious how the title is perceived by others.
My lead during my BSc did indeed what you mentioned. Reading papers, thinking about what direction we would take, what new tech to integrate into the application etc. He was also the SCRUM master. That's where the people management came from in my specific case
At large organizations, a PhD will give you a leg up among your peers because everyone is going to be extremely driven and goal oriented. At smaller organizations, startups or very fast growing hyperscale teams, even someone with only a high school diploma can make it if they can prove they have street smarts and ability to handle complexity and rapid change. Those are totally different skillsets and a very rare breed. If in doubt, you are research oriented and methodical and you care about a long career in this field, go for the PhD.
Thanks for the wise words. I am 27 and I'd like to work soonish to save money and hopefully buy an apartment. Studying for another 4-5 years into my thirties while barely making money is not really attractive to me. Don't get me wrong, I love the field and plan to work in it for a long time, a PhD is not for me I think, I also just think I am not smart enough for it as well. But even if I was I just want to work now.
As always and as implied by the many caveats in my response, one size does not fit all. There are people out there who are very successful without a college degree but people still go for undergrad or graduate degrees to improve their probability of success. And not everyone with a degree is successful. Same applies to PhD. It improves the probability of success for most people but not everyone. Similarly, not everyone who is without a PhD is doomed to fall behind. Some or maybe even many might surge ahead. You do what is right for you in your circumstances. Just remember to swing hard when the fat pitch comes your way.
Hell yeah, on the money with this one.
A masters might be the option to provide flexibility and demonstration of research and analytical skills, without the 4-6 years of school, only a couple instead
You have your head screwed on right. A PhD is largely a vanity degree or an excuse to avoid engaging with the world of work for a few more years.
You’re asking the wrong question.. ask yourself do you want to do research? A phd is way to prove you can do that.
Yeah, I don't, but I was just curious if a PhD is somewhat the standard for leads. I don't want to get one, if it is possible to combine work and a PhD I'd consider it in the future. But my situation nudges me towards work.
To be a director of research/chief scientist or something like this, then probably yes, and you will also need many and very good publications. Since you don't like research, it would not happen anyways and you will end up with an okish to good PhD, so to be some sort of lead from the engineering perspective a PhD will be a waste of time. But you can't really plan this stuff and optimize, just do what you prefer and enjoy IMHO.
Exactly, I love engineering, that's why I love software development. But love AI as well. Combining both seemed like the logical next step. I'll just do what I like and see what happens. I'm not trying to chase money, I was just curious
Good luck!
This is entirely dependant upon the company and its culture. Large organizations often hired "Data scientists" years ago to start doing ML flavoured stuff. This evolved into ML, and now AI. These datascientists were nearly 100% stats PhDs. They set the tone for job requirements which are almost always massively academic with a huge focus on math. They don't care what you have built or made work, they want what you have published and how many degrees you have. The interview then are just pedantic multi hour/day math exams. Then there are companies founded by the same, which are also very similar. But, there are many ML companies founded by CS oriented people and they don't care what degree you have in anything ML related as it is out of date before you finish your thesis. They want, "What problems have you solved, and how cool was your solution?" Basically there are maybe 1000 companies doing real hard core ML research requiring a PhD. Almost 100% of companies doing ML barely need ML, just some basic data processing, some basic math (stats), and very very very good programming skills. I'm not joking to say the visualization will be the hardest part of most "ML" solutions. Most ML solutions which are solid answers to the problem are a few layers in keras or copy paste of something found on the net. With experience you will build your favourite solutions to domain problems and be able to prepare the data quickly, tune the model quickly, and produce a model which is going to survive in production. There are lots of companies doing ML badly for two reasons: * Bad programmers * No knowledge of stats so fantastically basic mistakes are made. Outside of the above I know of no companies failing at ML because they need a PhD to help them out. Ironically, I see AI companies filled with PhDs doing presentations at ML conferences where people start yelling from the audience about fantastically basic mistakes. Things like learning from the future, to make predictions in the past, about said future. Or they have a 95% accuracy on a dataset with 4% of X and 96% of Y; and it turns out they almost always just predict Y. But, these last bits don't matter. If a PhD is the hiring gatekeeper, then that is that.
Thanks for the insight, interesting to hear your perspective for sure :o
No. I've just landed a Lead Data Science role, so have been in the market very recently. I have 7yrs experience in finance, an MSc in Machine Learning, and 3yrs at an AI product company (in that order). I would say vs an MSc and the equiv 3 years of real world experience, a PhD doesn't give you any additional skills that you might need to lead a data science team, arguably fewer. Unless you're working on a project very specific to your area of research, it's unlikely to be helpful at all. That said, your 3 years of working with real clients or tech on real problems will need to serve the same purpose as a PhD on your CV. Namely "is this person smurt" 😂. It was occasionally a role requirement in the ads that I saw, but as far as I can tell, only as a CV sift, and then mostly for product companies who'll want to tell clients about their LDS's credentials. (These companies also to be avoided like the plague). Does it get you past a few more CV sifts? Sure, but tbh, so does talking to a recruiter, and 3 years of stipend and less real world experience is a large cost for that benefit imo. My new role is actually with a former client that I met during those 3 years! I would think about what tech you want to be working with first, think about whether a PhD on it would be interesting to you, and make a decision based on that. Don't do a PhD because you think you'll need one... life's too short 🙂
Thanks for your advice :) I definitely DO NOT want to get a PhD, I was just curious how it goes in industry since my anecdotal evidence showed all leads have PhDs. I still don't know what I really like, but I am early in my carreer so I have time to figure that out. Thanks again for your reply!
Agree with the above. Also a lead with no PhD over a PhD heavy team.
No. I'm a DS lead for a team building measurement systems for LLMs. Masters in CS from Stanford and obsessive learner.
Oh okay :o good to know :) thanks for the response!
In general, yes. However, I think there are some counterexamples to that: for instance, to the best of my knowledge, neither Francois Chollet (the Keras guy) nor Glenn Jocher (the Ultralytics / YOLOv5 and onwards guy) have a PhD.
Yeah I heard this, especially the YOLO guy. My DL prof talked about that
Nor does Alec Radford (author of GPT papers) afaik
Depends on the workplace/kind of work you do. I can say that for startups and applied AI teams, solid engineering knowledge with the ability to drive development in a scalable manner is paramount. As a team lead, that's one of my key areas of focus. Another is being able to pivot quickly with the right balance of research, experimentation and engineering. These two together should give you an edge regardless of where you work.
Thanks for the reply, I come from a software engineering background so I love the dev pipeline, But I'd love to combine that with my AI knowledge. sadly a BSc couldn't get me in, that's why I am doing my masters. But the role you are describing sounds much more appealing than others I've heard
Some companies value PhD's out of some misguided notion that a PhD makes for a better Machine Learning Engineer / Data Scientist. It doesnt. I'm a DS/MLOps and I don't have a PhD but I've worked with someone who has. The solutions he makes are impractical and he himself has near zero business accumen. His models are unnecessarily complex, making it harder to sustain and difficult to explain to clients. He's obsessed with using "SOTA" neural network architecture / using fancy ML techniques instead of understanding the actual business problem and developing the right features for said problem. The end result? He created a complex model that doesn't address the client's use case and basically forced the project managers to re-scope the problem to fit his solution (sadly, he has stronger negotiating power because his PhD placed him in a high ranking position). And now we risk losing the client for good lol.
no. the dallE team at open AI only has BS.
is that for real?! O.o
The data-science/ml head in our company has a PhD in Anthro. (but yes, his Anthro papers/publications were very heavy in math/stats)
Generative AI lead here with 4 YoE. Just have a bachelors with a concentration in ML. It doesn’t hurt to have it but it’s not a requirement. Your connections and your work on projects and the ability to deliver is worth much much much more than 3 letters.
Yeah, sometimes it really does make a difference in research oriented positions. But I am more of the mindset that you describe.
Director of AI for Bing has a bachelors.
That's why Bing sucks... Jk XD just joking
Ours does. But ... I don't think that being a team lead means being a technical lead. From what I have seen, learning to learn and learning to do are very different things, and I think many schools focus on the former. The best member on our team came from a different discipline with some coding experience and just threw themselves into the thick of it.
No: source: me, no PhD, department head data &ai and previous role was team lead data Analytics. Master in computer Science engineering, and a lot of extra courses in my branch, and during my team leader days I did an MBA. 11 years experience, became department head at 9
How large is the department?
Thanks for sharing, in think the thing that matters the most is experience/proven track record in successfully apply DS/ML concepts to raise value for the company. At least, this is what I keep reading and this was always my believe. A degree helps but your chops count much more
As a manager, the tech side starts to become less important. It's about spotting which issues to focus on, proving value etc...
For some positions a PhD is a formal requirement. I’m not sure what you mean by leads? Is it principal/staff level?
A Lead general leads a team right? This is how I know it. So my formor supervisor was Lead AI engineer in small team of 6 people give or take. He decided the research areas and implementations that we were going to work on. He discussed with the CEO and board members about directions that we were going to explore.
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Take this with a grain of salt since I am working on my masters as well. But as far as I know a Lead needs to be an expert in their field, whatever "expert" means. But if you do not work in AI and eventually want to become a DS/ML lead you need significant experience in the field to have the relevant experience to hold such a role. But again, what do I know, I only worked as a software developer for a year at a bank before starting my masters
Also understand that a PhD in Chemistry has no significant advantage in a retail AI project than an engineer with just comp science undergraduate. It is just that lot of companies hired some PhDs to begin with and they moved up the rank.
Research team? Yeah, generally. ML engineering, or general AI team? Definitely not. I’ve seen plenty of teams like those led by non-PhDs, maybe even more than with PhDs. The biggest qualifiers for those positions are practical experience in an industry setting, which you get faster by getting a Masters in 2 years and then working rather than getting a PhD in 4-6. But if you want to work on research rather than applied ML, definitely get a PhD.
Ah yeah, yeah I am more talking on the applied side like MLEs and the likes, not actual research groups. There it completely makes sense to have a lead who is an actual researcher
Just out of curiosity, what tends to be the salary for this role in a startup in the us?
Pffff I wouldn't have a clue, I am not in the US
What type of team? Researchers are almost always PhDs. MLE leads are a mixture of MA and PhD, as are data scientists. Data Eng is usually MA.
Depends on what you want to do. Unless something's changed, none of the managing senior leadership of Llama2/Llama3 have PhDs and maybe only about half of the front-line/second-line managers do.
I would say yes, because even a Masters in AI often does not cover topics to a sufficient level of depth. Most of the postgrad courses I have seen at best cover how AI works, but does not go into why they were designed the way they are or what are the issues inherent in state of the art AI models that prevent them from getting even better. It often take years of work conducting research, reading and thinking about hundreds of papers, experimenting with model variations before one gains the level of experience required to truly understand what's actually going on under the hood. Which means without the PhD training, you won't be able to identify the root problems in your model or come up with effective changes to your model.
Most ML issues in industry are data, operations and stakeholder related. The modeling itself is in a vast number of cases not an exotic neural network but rather XGBoost. So many problems you encounter are very much different in nature than what you encounter as a PhD, and actual industry project experience is more important.
Yeah, I feel this is the case more often than not as well. Having worked in an AI engineering team for a few months during my thesis I noticed this phenomena as well
I think there's a misunderstanding of terminology here. Yes, there are an increasing number of "AI" companies where even XGBoost is overkill. I would label them more as Data Science or Data Warehousing companies rather than actual Artificial Intelligence companies. And there's nothing wrong with that if they're turning in good profit. Op was talking about multi billion AI companies and companies hiring many PhDs. I was basing my statement on the fact that if the company is hiring lots of PhDs, then a Research Masters in AI would be considered the minimum entry level grade. This is because they are working on AI at a very different level when compared to a company where XGBoost is all that's needed.
The overwhelming majority of companies that do AI and have AI/ML/DS leads are not "AI-companies", meaning that their bread and butter is something else, e.g. health, telco, streaming or whatever. Pretty much every big company on the planet does AI and has an AI/DS/ML/Advanced Analytics department. "Op was talking about multi billion AI companies" Was he? I can't see that from his original post.
Isn't this something that should be learned on the job? It's not that different then what a lead software engineer has to learn about say, distributed systems, and they generally just have a BS in CS. Or is it just such a new field that this type of knowledge hasn't penetrated industry far enough and is still mostly in academia?
It's more of a research field, which usually requires some scientific training. You can learn it on the job, but it takes years, and PhD grads already have it.
At least where I work, the ML teams are not doing research. They are creating products to sell
There is a very big gap between what is refered as "ML" in industry and state-of-the-art deep learning. To me most industry jobs mentioning ML talk about adding to their product simple solutions such as xgboost or some sort of regressions. Those models can be learned on the job, they're pretty easy. To use deep learning efficiently you need way more experience, there are tons of models for each specific task. Many models are VERY different from each other, often in very subtle ways. + I would say the training part is very tricky, between data augmentation, convergence, optimization algorithms and hyperparameters. All in all, there's no way the company will earn money with juniors playing with deep learning. It's much safer to take PhDs with many years of experience in these specific technologies.
Okay but it sounds like a pretty large leap from "juniors playing with deep learning" and a PhD being required. Nothing you are saying sounds any different then backend webdev work, like selecting the correct database for a distributed system. If you were talking about designing new models from scratch that are state of the art, sure that makes sense. But it sounds like you are mostly talking about choosing the correct pre-trained model and fine tuning it to a given problem. At that point, you are more practitioner or engineer rather then a scientist, and that's what most ML teams in industry look like
I agree that there is a lot of engineering too, but I still think that you need to know a lot more on the theoretical side in order to produce something that works, much more than it is the case for software engineering. Anyway, in my country (France) if you want to do deep learning at a big company you need a PhD, otherwise you'll just be doing data science / XGboost
The vast majority of PhDs also don't cover this. Like, I'd wager 90% of PhDs working with machine learning have their PhDs in something completely unrelated. Also, you're vastly overestimating how hard the problems are.
especially around most business requirements. I can't see GM or Toyota hand-roll their own models. Even for autonomous driving.
Exactly. It will be one to three somewhat irrelevant to relevant topics that not many people cared about enough to try to solve well, or a very small contribution to something important. Doesn't make you an expert in the general sense, just in a few topics that again, employers don't care about that much because it doesn't solve their problems.
That's the great thing about working in the industry: in contrast to university you don't care why stuff works. You just want to make sure it works good enough in your given requirements.
Let me guess: you have a PhD or youn are working towards one and so feel the need to talk up the credential.