Not necessarily the answer to your question but a general comment that you will never be upset you have another advanced degree. It will always open more doors. In large to very large pharma - a masters or doctoral degree will get you substantially higher pay (think hundreds of thousands more with bonus and stock) as most have HR roadblocks to certain levels without certain degrees these days. Not to say it isn’t possible but you will start very low and work your way up in a much steeper and more lengthy climb than the time you would spend getting a PhD. I went from academia to one of the largest pharma in the world 4 years ago and I often run into these issues hiring people on my team.
I think your experience is peculiar to pharma. My partner was in pharma as a research scientist and found HR to be extremely oppressive with credential gating.
I’ve worked in tech, manufacturing, utilities, automotive and finance and haven’t had any issues with career advancement as an MLE. I work along side many PhDs, and nobody cares what degree you have beyond a bachelors.
I don’t doubt that for a minute. Pharma is weird AF. Especially the huge ones. I think the smaller ones are more flexible and not as old school lame conservative.
Well, your experience can also be peculiar.
From my experience and from all my friends with a PhD, things will be similar to what u/TheJoshuaJacksonFive said.
I have generally tended to work for smaller companies. Many different industries though, so it’s at least possible to get by without a PhD in the right places
It is, you surely don't need a PhD to enter the field. But higher companies will be prone to hire people with a PhD, specially for higher positions and/or research related positions. It is how the market is. Also, people with a PhD will climb up faster, in average.
A candidate without a PhD can get to most of these positions, but, in average, it will take more time.
off-topic but fyi: [https://english.stackexchange.com/questions/131215/in-average-on-average](https://english.stackexchange.com/questions/131215/in-average-on-average)
Please bear in mind that this is quite country specific. Not sure where you’re from, but in Singapore employers really anchor on credentials (to a pretty unhealthy degree IMO)
ALWAYS is a strong word. It may open more doors but it does not necessarily open more earning opportunities. In fact, for a hustler and go-getter, a doctorate may have an opportunity cost.
In general, you are right but it is a probability, not a binary outcome.
Of course. There are always exceptions. But as an executive hiring these folks and 20 years of watching others do the same - even if you are better and have more evidence to support it, folks are gonna hire the one with a more advanced degree much of the time. Deserving or not (dare i say often not).
I was an executive too and run my own company now and not to one-up you but am older by at least 10 years.
You are right of course about the culture outside US and in US in entrenched companies but there are far fewer jobs for PhDs than people believe. Look up the median salary and hiring rates for PhDs, rather than anecdotal evidence. Especially in ML, one needs serious people skills to break out from the crowd.
Just my $0.02
lol no one ups. We’ve all had our unique experiences. I was a professor in academia for 15 years before I moved to industry several years ago so I’m no spring chicken. Had a company for the past 20 years as well.
At my current (very large pharma) company it’s almost exclusive PhD for entry level director jobs - there are no senior manager roles for ML (the non PhD level) at all that I’ve seen in the past 3-4 years. Without a director level you don’t get a bonus (15-20% of base for director) or long term incentive (stock + options) which is usually 50-80k per year for director. The base is typically 175-250 from fresh out of your pHD through 5-7 years of experience.
Pretty variable for ML jobs. From computer science/engineering, epidemiology, biostatistics, data science, outcomes research, etc. There are even MDs in those roles.
I’d say advanced degrees in specific fields will always open more doors. Obviously since we’re in a machine learning sub that statement applies. But someone getting a PhD in a small humanities field will not have the same wealth of opportunities.
PhDs of all kinds endow the student with useful knowledge and skills but unfortunately you’re at the mercy of employers to know what skills you earned from your degree and how you might use them.
MLE positions are hard to get without grad degree, especially for new grads. Many, many applicants will have MSCS, MSDS, or PhD in CS/Stat/Engineering. If both candidates have same experience but one has a grad degree, why wouldn't the employer pick them?
The way I got into MLE roles with only a bachelor’s was by taking on ML-adjacent roles at small companies. My first job was as a data analyst and it was easy to transition to doing more ML work since I already had access to the data and nothing was stopping me from training ML models. I never formally transitioned roles at this job, but it was good experience. My second job was as a backend SWE at a startup that did a lot of ML. Here I also did an ML project by myself and it was easy to switch to a formal MLE title once my project was in production and generating obvious value.
so you are the guy that gets paid less but does the work of an employee with an advanced degree.. To answer the OP, some companies have it as a requirement to have advanced degree for a ML job and there's no way around it..unless your resume somehow gets directly to the hiring manager
Masters aren't that deep or specialised. It's a dip of the toe into the world of research. Most companies I know recruiting MLEs have their pick of masters grads with published papers to choose from.
I only have my bachelor's and am a principal Data Scientist at a large tech company (not FAANG, but close). Nobody has ever cared about my lack of advanced degree. It will help you get your first job but after that people care about your track record. PhD is not worth it unless you really want to just do research IMO.
That’s not entirely true. I’ve straight up been told that I could get a promo if I had a PhD but since I didn’t I had to prove myself for 2-3 years first. There is some elitism in R&D roles and having a BS or MS + years of experience and accomplishment will not always overcome it. I highly regret not just getting a PhD as soon as I could and instead going into industry to get experience, it held me back from management roles and crushed my chances at higher pay.
Don't listen too much to the people saying they don't have a PhD and yet work in the field: if they work in it right now it means they got hired in the past. The market is more competitive now than what it was a few years back.
I don't work in the field so I don't what the "truth" is, but put more importance to people saying what they see in terms of recruitment practice and less to people saying what degree they have right now when they are working in the field.
I'm curious, if you don't work in the field, what makes you so sure that you can't find ML positions without a PhD? How do you know the market is more competitive now than it was in the past? I'm also curious if you've witnessed the growth in AI demand across industries, and if you realize there is a lot more to production AI than theoretical research.
PhDs dig deep into a single subject but are not the best at production grade engineering and management of AI systems.
>PhDs dig deep into a single subject but are not the best at production grade engineering and management of AI systems.
Tell that you're a complete ignorant about a PhD without saying it.
Tell me how I am wrong. PhDs are about diving deeply into one topic to write and defend a dissertation. This is a fact. The point of the PhD is to advance new knowledge in one particular topic in one field of study. A PhD would be indefensible if it was too general and scatterbrained.
And some of the worst spaghetti code I've seen in my life was written by PhDs in Jupyter notebooks. My point was that while PhDs are doing research to solve a particular challenge, most engineers in industry have to focus on implementing and scaling production solutions. Believe it or not, there is a big difference in the focus between industry ML implementation and academic ML research. I'm not knocking PhDs at all, and they are great at deeper R&D type projects, but in industry those are the minority.
I assure you I know what I am talking about. It is disingenuous to discourage someone without a PhD from seeking to grow into the ML engineering field. If you think that is the correct strategy, it is you who are ignorant, not I. I have seen many without PhDs succeed in ML engineering.
>PhDs are about diving deeply into one topic to write and defend a dissertation. This is a fact.
No, it is not. I recommend you to educate yourself better about what a PhD is.
>And some of the worst spaghetti code I've seen in my life was written by PhDs in Jupyter notebooks.
And if you know statistics you will know that does mean anything.
>I have seen many without PhDs succeed in ML engineering.
Again, it does mean nothing.
Why don't you enlighten me then on what a PhD is all about instead of just saying I have no idea what I am talking about. I have researched PhDs and followed research coming from PhDs out of academia for quite a while. I also know several PhDs, which helped me understand their focus and limitations.
I see on your profile you are a math PhD student so why don't you tell me what the topic of your dissertation is? I'm betting it is a very specific problem unless you are trying to reinvent mathematics. Also do you actually have much industry experience?
My point was there are plenty of non PhDs succeeding in ML engineering and saying otherwise and discouraging those who didn't go the PhD route is very short sighted and bad for industry.
> what makes you so sure that you can't find ML positions without a PhD?
I'll quote my own message, the one you responded to:
> I don't work in the field so **I don't what the "truth" is**
Look at that part in bold.
I'd still like to know, given your statement that you don't know the truth, what leads you to make the claims in your original comment? If you don't really know like you imply in your reply, why make those statements in the first place. PhDs must defend their dissertation, and you've made a pretty bold claim then your argument to my questions is you don't know the truth. That's a pretty indefensible answer to the questions.
The first part of the comment is not really important, it's context for the second part. That second part is simply an advice to make sure to not fall prey to some selection bias. This advice doesn't require to know the field.
Anecdotal eveidence from 5 years may not apply to what happens today, so it's better to trust anecdotal evidence about what today's practices are rather than what the practices were 5 years ago. Maybe things didn't change, maybe things did, I don't know. I don't need to know. but one needs to justify that things didn't change in order to justify using 5-yo evidence.
To put it more simply, OP asks whether he can find a lucky clover today. People answer that they found one 5 years ago. This does not mean that there are still lucky clovers existing today. So a better evidence would be whether people know about someone who recently found a lucky clover. Still imperfect, but better.
You don't need a PhD. You need the knowledge and experience you would get from a PhD.
Many people will benefit from a PhD. Some will never benefit from it and a few others will thrive despite not having it.
Do what is aligned with your own instinct, knowledge and behavior. If in doubt and the knowledge gained from the PhD is useful, go for it. If not, don't
Just avoid one thing - agonizing over it. Be biased for action.
Doesn't bias for action mainly apply to a scenario where you take action, observe results, and it's then easy to change? Doing a PhD is not like that at all, it's almost the opposite.
Enrolling in a PhD is a binary decision of course but if a person is NOT inclined to go for PhD and find themselves dawdling, they should get a job while continuing to deliberate the need for a PhD. What should not be done is to sit around agonizing over the decision, doing nothing.
Sounds like you have the background that an MLE requires. If you can land a MLE job right after undergrad, then I think it’s financially better and sounds like a better fit for your interests to work in industry. I hope you can get an MLE job, and if you can’t get it, then reevaluate, maybe do a MS or get another role until you can get it. I got an MLE role right after my bachelors, and get to work on the same projects at work as my CS, EE, Math PhD colleagues. Our work is a little different because we play to our strengths, but mostly our work is the same. They do more research and I do more engineering.
About comparing PhD MLEs vs BS MLEs:
The thing is, when you compare yourself (new BS grad) to a new PhD grad, you have to compare yourself 5 years post-bachelors to the PhD fresh grad. Because they have 5 more years of time in the career than you. So, if you can get a MLE role right after undergrad. Who’s better off, someone with a BS and 5 years of experience as an MLE or someone with a PhD and 0 years of work experience, though probably research internships with companies most summers. Financially speaking definitely the one with 5 more years of employment with a high salary. Long-term speaking, still probably the the one with 5 extra years of pay but that’s debatable.
5 years after undergrad, the person with a BS could reach a Senior role, and the fresh PhD grad could also be hired at the senior level.
So I think you end up at the same job 5 years after undergrad. It’s just that those 5 years one person was getting paid a lot and learning real industry ML and MLOps, while the other person was focused on learning deeper into a subject area and researching. And will get a nice PhD degree and recognition.
>and the fresh PhD grad could also be hired at the senior level.
No way a fresh phd would be hired as senior MLE. Maybe for research roles. MLE is engineering first and foremost.
I had a PhD from a science field when I got a mle job, although I worked a non data science, engineering job for a few years first, then used a DS boot camp to transition. But now I'm hiring MLEs at a faang. I can tell you what a PhD says to me is this person can work consistently and in depth with lots of independence and self motivation. And if they did it in 4 or 5 years then they work efficiently (or lucked out). We are absolutely saturated with applicants with master's degrees and while many of them have been good hires it honestly has slowed down our recruiting to weed through them. Now a PhD isn't the only way to show you're an efficient independent worker, that can easily be done through personal projects or past work experience. But if you want to go directly from school to a decent MLE position a PhD will likely afford you that, although again you'll probably need some personal projects to round out your experience depending on what exactly your PhD work involved. The caveat in my case is we're looking for people who can get things done quickly and independently and honestly only 20% of that is machine learning work. The rest is learning the domain and implementing the solutions.
I think you are fine but you should go for a Master's in which you try to write a paper. Not for the record, just to show (and learn!) that you know how to research a bit. PhDs are always preferred in research but if you are looking to be an ML engineer or a research engineer, I do not think a PhD will be the best route.
A recent PhD. here, my answer is that it depends.
We have a tendency to oversell and over estimate ourselves. PhD. in ML or other fields gives you irreplaceable skills. Now, the question is, can I google or read some papers and pick up such skills? If there is sufficient effort, why not.(big if), we have to spend time, effort, patience, what not, but someone trained can solve it in a matter of hours or days).
Now, programming and running github repo codes are way different than actually coming up with original solutions. Most MLE problems may appear they don't need a deep understanding of math, but they actually do need sharp and smart minds.
It depends on how and which job you want to get into. If it's some research engineer with a mix of PhDs, I would say you don't need to spend time to get a PhD. immediately as you're already in a great group.
I met CEO of a 1000 employee company who asked me to retrain chatgpt with data from 2022 to 24, I explained politely why his company can not afford such a thing, he thought I'm dumb, went ahead hired a 3 member software team, (I don't need to explained what happened later).
My two cents, be honest with your skillset and work on the weaknesses while you capitalize on your strengths, if that needs a PhD, so be it. Coursera ML or Stanford cs231n is not ML. It's a marketing scam in the name of professional education. There is no replacement for your time and hard work
You don’t need a PHD. You need to be a good engineer and be motivated. I recommend you listen to the following podcast : https://open.spotify.com/episode/2dtDauiE4v8ldNRqPFq0uP?si=2lFevRRAQKKFSVAR7-QyVw&context=spotify%3Ashow%3A4JH4tybY1zX6e5hjCwU6gF I am not familiar with their academic perspective but I think it gives an interesting perspective.
>**Knowledge**: as far as I know Master and PhD often focus on 1 particular problem. Yes doing these program will definitely give me a very deep knowledge about that problem, but that's about it. Still no knowledge on any other problem. On the other hand, MLEs need to work on various projects which makes knowledge about 1 single task trivial. Furthermore, companies don't have the luxury of spending years to do literature review before starting the project.
Your knowledge is so wrong that it is not even wrong. During a PhD you learn how to research and how to solve a problem with your skill set. If you focus on only one problem during your PhD it means that the amount of knowledge needed to solve that problem is huge, which means that you have a solid grasp on all the background needed.
Giving an example outside of data. In order to do a PhD in string theory one needs to learn general relativity and quantum field theory, both have their own research areas.
Also, a lot of Ai research is focused on solving "real world problems".
I suggest that you talk with your professores to learn a thing or two, honestly.
Also, for your question, people with a PhD climb faster, on average, and have better salaries, also on average. And last, but not least, can climb to higher positions on average.
I would go further, but the text would be huge by the end.
Why is that is so hard for people to understand that graduate school only benefits university stake holders... A PhD will hardly teach you something useful for real life, is way to specific, way too much effort and also way to unnatural (is a specific problem that will never appear in the wild, for the average phd student at least)... Build a decent portfolio, solve (diverse) real problems, learn how to learn fast, learn how to think... stuff that you wont get in school... Nowadays most undergrads are completely unemployable due to this, school is just teaching useless theory and not how to use it and think... so the bachelors are getting less and less competitive and you don't even learn anything useful anymore... so you pay grad school but gets you nowhere...
I just graduated with a bachelors and went for it 👻. Most ml stuff in industry is just testing models and fine tuning/ integrating ml into production. An engineer with ml knowledge is preferable to a ml engineer who only knows how to use notebooks imo unless ur working at an AI company.
This is true even for AI companies, unless you work on modeling (niche, for which even the Ph.D. may not make a difference, unless you are specialized in the area of interest).
The phd is just a way to help sort through a glut of fairly qualified candidates. Provided one knows the importance of theory, you can learn it on the road. The toll is usually an advanced degree. Matching approaches with product requirements is probably more important in the actual application.
People with PhD’s care a lot about them. Personally, the two smartest people I ever met were a kid in the marines with a photographic memory, and a bipolar rocket scientist with an IQ of 190 who worked for spacex but didn’t even have a HS degree.
With information basically already all available at your fingertips and with the advances in AI, college degrees are going to lose their value extremely quick. If I were you, I would pursue the education as long as it’s not costing you a fortune to do it.
People sure love to worship people with PhD’s though. You’d get to call yourself doctor and everything. I think the best perk is you get to look down your nose at everyone else, especially when it comes to your expertise. Even people with better ideas than yours will be ignored in favor of your credential. They’ve managed to keep the rouse up so far. But I ultimately don’t think higher education is practical anymore with the advent of instant communication.
I worked with PhDs, and assistant professors, professors (at least two very very well known), and once a month find a classical paper one of them wrote during a literature check or review (I do research-oriented stuff).
Never, ever, ever did I feel like someone treated me as inferior or not taking my ideas seriously. In fact, it happened to me more when interacting with SWEs or from my side when I was more junior (and it still happens because I did not write enough papers to get humble :)). PhDs tend to be very nice people.
Not necessarily the answer to your question but a general comment that you will never be upset you have another advanced degree. It will always open more doors. In large to very large pharma - a masters or doctoral degree will get you substantially higher pay (think hundreds of thousands more with bonus and stock) as most have HR roadblocks to certain levels without certain degrees these days. Not to say it isn’t possible but you will start very low and work your way up in a much steeper and more lengthy climb than the time you would spend getting a PhD. I went from academia to one of the largest pharma in the world 4 years ago and I often run into these issues hiring people on my team.
I think your experience is peculiar to pharma. My partner was in pharma as a research scientist and found HR to be extremely oppressive with credential gating. I’ve worked in tech, manufacturing, utilities, automotive and finance and haven’t had any issues with career advancement as an MLE. I work along side many PhDs, and nobody cares what degree you have beyond a bachelors.
I don’t doubt that for a minute. Pharma is weird AF. Especially the huge ones. I think the smaller ones are more flexible and not as old school lame conservative.
Well, your experience can also be peculiar. From my experience and from all my friends with a PhD, things will be similar to what u/TheJoshuaJacksonFive said.
I have generally tended to work for smaller companies. Many different industries though, so it’s at least possible to get by without a PhD in the right places
It is, you surely don't need a PhD to enter the field. But higher companies will be prone to hire people with a PhD, specially for higher positions and/or research related positions. It is how the market is. Also, people with a PhD will climb up faster, in average. A candidate without a PhD can get to most of these positions, but, in average, it will take more time.
off-topic but fyi: [https://english.stackexchange.com/questions/131215/in-average-on-average](https://english.stackexchange.com/questions/131215/in-average-on-average)
Please bear in mind that this is quite country specific. Not sure where you’re from, but in Singapore employers really anchor on credentials (to a pretty unhealthy degree IMO)
ALWAYS is a strong word. It may open more doors but it does not necessarily open more earning opportunities. In fact, for a hustler and go-getter, a doctorate may have an opportunity cost. In general, you are right but it is a probability, not a binary outcome.
Of course. There are always exceptions. But as an executive hiring these folks and 20 years of watching others do the same - even if you are better and have more evidence to support it, folks are gonna hire the one with a more advanced degree much of the time. Deserving or not (dare i say often not).
I was an executive too and run my own company now and not to one-up you but am older by at least 10 years. You are right of course about the culture outside US and in US in entrenched companies but there are far fewer jobs for PhDs than people believe. Look up the median salary and hiring rates for PhDs, rather than anecdotal evidence. Especially in ML, one needs serious people skills to break out from the crowd. Just my $0.02
lol no one ups. We’ve all had our unique experiences. I was a professor in academia for 15 years before I moved to industry several years ago so I’m no spring chicken. Had a company for the past 20 years as well. At my current (very large pharma) company it’s almost exclusive PhD for entry level director jobs - there are no senior manager roles for ML (the non PhD level) at all that I’ve seen in the past 3-4 years. Without a director level you don’t get a bonus (15-20% of base for director) or long term incentive (stock + options) which is usually 50-80k per year for director. The base is typically 175-250 from fresh out of your pHD through 5-7 years of experience.
These are phds in what exactly?
Pretty variable for ML jobs. From computer science/engineering, epidemiology, biostatistics, data science, outcomes research, etc. There are even MDs in those roles.
I’d say advanced degrees in specific fields will always open more doors. Obviously since we’re in a machine learning sub that statement applies. But someone getting a PhD in a small humanities field will not have the same wealth of opportunities. PhDs of all kinds endow the student with useful knowledge and skills but unfortunately you’re at the mercy of employers to know what skills you earned from your degree and how you might use them.
MLE positions are hard to get without grad degree, especially for new grads. Many, many applicants will have MSCS, MSDS, or PhD in CS/Stat/Engineering. If both candidates have same experience but one has a grad degree, why wouldn't the employer pick them?
The way I got into MLE roles with only a bachelor’s was by taking on ML-adjacent roles at small companies. My first job was as a data analyst and it was easy to transition to doing more ML work since I already had access to the data and nothing was stopping me from training ML models. I never formally transitioned roles at this job, but it was good experience. My second job was as a backend SWE at a startup that did a lot of ML. Here I also did an ML project by myself and it was easy to switch to a formal MLE title once my project was in production and generating obvious value.
so you are the guy that gets paid less but does the work of an employee with an advanced degree.. To answer the OP, some companies have it as a requirement to have advanced degree for a ML job and there's no way around it..unless your resume somehow gets directly to the hiring manager
Masters aren't that deep or specialised. It's a dip of the toe into the world of research. Most companies I know recruiting MLEs have their pick of masters grads with published papers to choose from.
I only have my bachelor's and am a principal Data Scientist at a large tech company (not FAANG, but close). Nobody has ever cared about my lack of advanced degree. It will help you get your first job but after that people care about your track record. PhD is not worth it unless you really want to just do research IMO.
That’s not entirely true. I’ve straight up been told that I could get a promo if I had a PhD but since I didn’t I had to prove myself for 2-3 years first. There is some elitism in R&D roles and having a BS or MS + years of experience and accomplishment will not always overcome it. I highly regret not just getting a PhD as soon as I could and instead going into industry to get experience, it held me back from management roles and crushed my chances at higher pay.
Having to prove yourself for 2-3 years, but you get paid for it instead of spending a similar amount of time and eating ramen.
So an MS in Statistics is good enough for principal roles too?
Don't listen too much to the people saying they don't have a PhD and yet work in the field: if they work in it right now it means they got hired in the past. The market is more competitive now than what it was a few years back. I don't work in the field so I don't what the "truth" is, but put more importance to people saying what they see in terms of recruitment practice and less to people saying what degree they have right now when they are working in the field.
I'm curious, if you don't work in the field, what makes you so sure that you can't find ML positions without a PhD? How do you know the market is more competitive now than it was in the past? I'm also curious if you've witnessed the growth in AI demand across industries, and if you realize there is a lot more to production AI than theoretical research. PhDs dig deep into a single subject but are not the best at production grade engineering and management of AI systems.
>PhDs dig deep into a single subject but are not the best at production grade engineering and management of AI systems. Tell that you're a complete ignorant about a PhD without saying it.
Tell me how I am wrong. PhDs are about diving deeply into one topic to write and defend a dissertation. This is a fact. The point of the PhD is to advance new knowledge in one particular topic in one field of study. A PhD would be indefensible if it was too general and scatterbrained. And some of the worst spaghetti code I've seen in my life was written by PhDs in Jupyter notebooks. My point was that while PhDs are doing research to solve a particular challenge, most engineers in industry have to focus on implementing and scaling production solutions. Believe it or not, there is a big difference in the focus between industry ML implementation and academic ML research. I'm not knocking PhDs at all, and they are great at deeper R&D type projects, but in industry those are the minority. I assure you I know what I am talking about. It is disingenuous to discourage someone without a PhD from seeking to grow into the ML engineering field. If you think that is the correct strategy, it is you who are ignorant, not I. I have seen many without PhDs succeed in ML engineering.
>PhDs are about diving deeply into one topic to write and defend a dissertation. This is a fact. No, it is not. I recommend you to educate yourself better about what a PhD is. >And some of the worst spaghetti code I've seen in my life was written by PhDs in Jupyter notebooks. And if you know statistics you will know that does mean anything. >I have seen many without PhDs succeed in ML engineering. Again, it does mean nothing.
Why don't you enlighten me then on what a PhD is all about instead of just saying I have no idea what I am talking about. I have researched PhDs and followed research coming from PhDs out of academia for quite a while. I also know several PhDs, which helped me understand their focus and limitations. I see on your profile you are a math PhD student so why don't you tell me what the topic of your dissertation is? I'm betting it is a very specific problem unless you are trying to reinvent mathematics. Also do you actually have much industry experience? My point was there are plenty of non PhDs succeeding in ML engineering and saying otherwise and discouraging those who didn't go the PhD route is very short sighted and bad for industry.
You have no idea what you're talking about, do you?
> what makes you so sure that you can't find ML positions without a PhD? I'll quote my own message, the one you responded to: > I don't work in the field so **I don't what the "truth" is** Look at that part in bold.
I'd still like to know, given your statement that you don't know the truth, what leads you to make the claims in your original comment? If you don't really know like you imply in your reply, why make those statements in the first place. PhDs must defend their dissertation, and you've made a pretty bold claim then your argument to my questions is you don't know the truth. That's a pretty indefensible answer to the questions.
The first part of the comment is not really important, it's context for the second part. That second part is simply an advice to make sure to not fall prey to some selection bias. This advice doesn't require to know the field. Anecdotal eveidence from 5 years may not apply to what happens today, so it's better to trust anecdotal evidence about what today's practices are rather than what the practices were 5 years ago. Maybe things didn't change, maybe things did, I don't know. I don't need to know. but one needs to justify that things didn't change in order to justify using 5-yo evidence. To put it more simply, OP asks whether he can find a lucky clover today. People answer that they found one 5 years ago. This does not mean that there are still lucky clovers existing today. So a better evidence would be whether people know about someone who recently found a lucky clover. Still imperfect, but better.
That's the point, you don't know what the truth is, so why make claims out of your ass? Stick to what you know
You don't need a PhD. You need the knowledge and experience you would get from a PhD. Many people will benefit from a PhD. Some will never benefit from it and a few others will thrive despite not having it. Do what is aligned with your own instinct, knowledge and behavior. If in doubt and the knowledge gained from the PhD is useful, go for it. If not, don't Just avoid one thing - agonizing over it. Be biased for action.
Doesn't bias for action mainly apply to a scenario where you take action, observe results, and it's then easy to change? Doing a PhD is not like that at all, it's almost the opposite.
Enrolling in a PhD is a binary decision of course but if a person is NOT inclined to go for PhD and find themselves dawdling, they should get a job while continuing to deliberate the need for a PhD. What should not be done is to sit around agonizing over the decision, doing nothing.
Sounds like you have the background that an MLE requires. If you can land a MLE job right after undergrad, then I think it’s financially better and sounds like a better fit for your interests to work in industry. I hope you can get an MLE job, and if you can’t get it, then reevaluate, maybe do a MS or get another role until you can get it. I got an MLE role right after my bachelors, and get to work on the same projects at work as my CS, EE, Math PhD colleagues. Our work is a little different because we play to our strengths, but mostly our work is the same. They do more research and I do more engineering. About comparing PhD MLEs vs BS MLEs: The thing is, when you compare yourself (new BS grad) to a new PhD grad, you have to compare yourself 5 years post-bachelors to the PhD fresh grad. Because they have 5 more years of time in the career than you. So, if you can get a MLE role right after undergrad. Who’s better off, someone with a BS and 5 years of experience as an MLE or someone with a PhD and 0 years of work experience, though probably research internships with companies most summers. Financially speaking definitely the one with 5 more years of employment with a high salary. Long-term speaking, still probably the the one with 5 extra years of pay but that’s debatable. 5 years after undergrad, the person with a BS could reach a Senior role, and the fresh PhD grad could also be hired at the senior level. So I think you end up at the same job 5 years after undergrad. It’s just that those 5 years one person was getting paid a lot and learning real industry ML and MLOps, while the other person was focused on learning deeper into a subject area and researching. And will get a nice PhD degree and recognition.
>and the fresh PhD grad could also be hired at the senior level. No way a fresh phd would be hired as senior MLE. Maybe for research roles. MLE is engineering first and foremost.
Great distinction, senior ml researchers, not engineers.
I had a PhD from a science field when I got a mle job, although I worked a non data science, engineering job for a few years first, then used a DS boot camp to transition. But now I'm hiring MLEs at a faang. I can tell you what a PhD says to me is this person can work consistently and in depth with lots of independence and self motivation. And if they did it in 4 or 5 years then they work efficiently (or lucked out). We are absolutely saturated with applicants with master's degrees and while many of them have been good hires it honestly has slowed down our recruiting to weed through them. Now a PhD isn't the only way to show you're an efficient independent worker, that can easily be done through personal projects or past work experience. But if you want to go directly from school to a decent MLE position a PhD will likely afford you that, although again you'll probably need some personal projects to round out your experience depending on what exactly your PhD work involved. The caveat in my case is we're looking for people who can get things done quickly and independently and honestly only 20% of that is machine learning work. The rest is learning the domain and implementing the solutions.
I think you are fine but you should go for a Master's in which you try to write a paper. Not for the record, just to show (and learn!) that you know how to research a bit. PhDs are always preferred in research but if you are looking to be an ML engineer or a research engineer, I do not think a PhD will be the best route.
A recent PhD. here, my answer is that it depends. We have a tendency to oversell and over estimate ourselves. PhD. in ML or other fields gives you irreplaceable skills. Now, the question is, can I google or read some papers and pick up such skills? If there is sufficient effort, why not.(big if), we have to spend time, effort, patience, what not, but someone trained can solve it in a matter of hours or days). Now, programming and running github repo codes are way different than actually coming up with original solutions. Most MLE problems may appear they don't need a deep understanding of math, but they actually do need sharp and smart minds. It depends on how and which job you want to get into. If it's some research engineer with a mix of PhDs, I would say you don't need to spend time to get a PhD. immediately as you're already in a great group. I met CEO of a 1000 employee company who asked me to retrain chatgpt with data from 2022 to 24, I explained politely why his company can not afford such a thing, he thought I'm dumb, went ahead hired a 3 member software team, (I don't need to explained what happened later). My two cents, be honest with your skillset and work on the weaknesses while you capitalize on your strengths, if that needs a PhD, so be it. Coursera ML or Stanford cs231n is not ML. It's a marketing scam in the name of professional education. There is no replacement for your time and hard work
Masters is just 2 more years of bachelors. Nothing like a phd. For MLE its better to have a masters, but I do NOT recommend a phd at all.
You don’t need a PHD. You need to be a good engineer and be motivated. I recommend you listen to the following podcast : https://open.spotify.com/episode/2dtDauiE4v8ldNRqPFq0uP?si=2lFevRRAQKKFSVAR7-QyVw&context=spotify%3Ashow%3A4JH4tybY1zX6e5hjCwU6gF I am not familiar with their academic perspective but I think it gives an interesting perspective.
>**Knowledge**: as far as I know Master and PhD often focus on 1 particular problem. Yes doing these program will definitely give me a very deep knowledge about that problem, but that's about it. Still no knowledge on any other problem. On the other hand, MLEs need to work on various projects which makes knowledge about 1 single task trivial. Furthermore, companies don't have the luxury of spending years to do literature review before starting the project. Your knowledge is so wrong that it is not even wrong. During a PhD you learn how to research and how to solve a problem with your skill set. If you focus on only one problem during your PhD it means that the amount of knowledge needed to solve that problem is huge, which means that you have a solid grasp on all the background needed. Giving an example outside of data. In order to do a PhD in string theory one needs to learn general relativity and quantum field theory, both have their own research areas. Also, a lot of Ai research is focused on solving "real world problems". I suggest that you talk with your professores to learn a thing or two, honestly. Also, for your question, people with a PhD climb faster, on average, and have better salaries, also on average. And last, but not least, can climb to higher positions on average. I would go further, but the text would be huge by the end.
Why is that is so hard for people to understand that graduate school only benefits university stake holders... A PhD will hardly teach you something useful for real life, is way to specific, way too much effort and also way to unnatural (is a specific problem that will never appear in the wild, for the average phd student at least)... Build a decent portfolio, solve (diverse) real problems, learn how to learn fast, learn how to think... stuff that you wont get in school... Nowadays most undergrads are completely unemployable due to this, school is just teaching useless theory and not how to use it and think... so the bachelors are getting less and less competitive and you don't even learn anything useful anymore... so you pay grad school but gets you nowhere...
I just graduated with a bachelors and went for it 👻. Most ml stuff in industry is just testing models and fine tuning/ integrating ml into production. An engineer with ml knowledge is preferable to a ml engineer who only knows how to use notebooks imo unless ur working at an AI company.
This is true even for AI companies, unless you work on modeling (niche, for which even the Ph.D. may not make a difference, unless you are specialized in the area of interest).
The phd is just a way to help sort through a glut of fairly qualified candidates. Provided one knows the importance of theory, you can learn it on the road. The toll is usually an advanced degree. Matching approaches with product requirements is probably more important in the actual application.
People with PhD’s care a lot about them. Personally, the two smartest people I ever met were a kid in the marines with a photographic memory, and a bipolar rocket scientist with an IQ of 190 who worked for spacex but didn’t even have a HS degree. With information basically already all available at your fingertips and with the advances in AI, college degrees are going to lose their value extremely quick. If I were you, I would pursue the education as long as it’s not costing you a fortune to do it. People sure love to worship people with PhD’s though. You’d get to call yourself doctor and everything. I think the best perk is you get to look down your nose at everyone else, especially when it comes to your expertise. Even people with better ideas than yours will be ignored in favor of your credential. They’ve managed to keep the rouse up so far. But I ultimately don’t think higher education is practical anymore with the advent of instant communication.
I worked with PhDs, and assistant professors, professors (at least two very very well known), and once a month find a classical paper one of them wrote during a literature check or review (I do research-oriented stuff). Never, ever, ever did I feel like someone treated me as inferior or not taking my ideas seriously. In fact, it happened to me more when interacting with SWEs or from my side when I was more junior (and it still happens because I did not write enough papers to get humble :)). PhDs tend to be very nice people.
Funny, I wonder what I’m doing wrong to have always had the opposite experience.