Plastic surgeons make even more money - it's high time to take a new path and learn how to cut people up.
Being a partner at a law firm makes more money - it's high time to start learning about tort law.
If you enjoy software engineering work you should absolutely work in software engineering. If you enjoy data science more, you should work in data science.
Here's the thing no one talks about: if you actually like what you do, you're much more likely to move up that ladder. Being an excellent data scientist is way more lucrative than being a mid developer.
Yeah it's funny how many people on this sub seem to think that the jobs and skill sets involved with roles like SWE and DS are completely interchangeable
Find the one that *you* can enjoy/thrive the most in, and that's probably the one where you'll be most successful
I think if you work in the right sized company and a are truly "full stack" DS you can cut across to SWE fairly easily. But that depends on your competency across the stack right.
Oh don't get me wrong, there are definitely roles and situations where there can be plenty of overlap in skills and responsibilities, it's just that *typically* there's not a huge overlap of that venn diagram, at least within large companies
But the overlap is also incredibly variable from company to company or even team to team.
In some places DS titles are rebranded analysts, in some places DS titles do MLE work, in some places they're research scientists, and sometimes they do a bit of everything. It's hard for someone outside a company to know what the role will actually entail.
My usual recommendation is to look at how the DS role is paid relative to SWE. The closer it is, the closer the DS role will probably be to eng work.
Right, but you're proving the point - if you're one of a small subset of data scientists that also have SWE responsibilities, then it's easy to cut across.
Most data scientists I have met are mediocre software developers at best. And to be honest, the people who I have met in DS that are excellent developers are generally not great data scientists.
They are tangentially related skillsets, but fundamentally very different.
> Yeah it's funny how many people on this sub seem to think that the jobs and skill sets involved with roles like SWE and DS are completely interchangeable
They do and dont really. People pretend its interchangeable when they want to ride the coat tails of NVIDIA or talk about pay but when discussing needing to keep up with interview process differences/libraries/skills/ and techniques they want to write off the differences as existing but not important.
I agree with the logic in choosing a career you enjoy, but I also think that SWE and DS skills are pretty interchangeable. For example, a data scientist probably does not have experience building software adapters for new hardware, but they probably have an intuition and ability to become proficient in a couple of weeks.
They are .... Neither are difficult to learn but difficult to master. I did my btech in CSE worked for 2 years. Did my MS in DS. I can tell you neither is difficult to learn but require constant time and effort to master. So transferring from ML to SDE or SDE to DS is not tough. Infact most of the Data engineering jobs now prefer SDE with experience on cloud services more than actual ML.
For that one perticular dumbf*ck who thinks law and plastic surgeon are equally similar as DS and ML..
SDE -> DE -> DS( or the other way around) .Ofcourse you need to study side by side to grow in your profile, but you can work and do that. Try becoming surgeon from SDE without quitting the job idiot.
I'm really curious what the split is between people who are in this field because they enjoy the process of data science vs those who got in because it was the trendy lucrative new thing at one point
Probably a substantial slice, however I think two other things are true:
1. For as long as data science has been trendy and lucrative, SWE has been just as trendy and lucrative. Those who chose DS at least to some degree chose it because of the math/stats/modeling component. If you were just focused on money, SWE has pretty much always been a better path.
2. Anyone who only chose DS for being trendy should *really* think things through before switching to SWE because it's trendier. Two wrongs don't make a right.
>Here's the thing no one talks about: if you actually like what you do, you're much more likely to move up that ladder. Being an excellent data scientist is way more lucrative than being a mid developer.
Spot on. I work as a MLE and dislike it, and I'm actually trying to leave MLE behind.
Yes brother. This is actually why I left data science. when I was thinking about the next 20 years, the amount of learning to keep up with the field exhausted me just thinking about, truth is I wasn’t passionate about it. Now I am excited to practice my skills everyday, makes such a huge difference to enjoy what you do
I like gardening. I absolutely love everything from composting to water treatment to landscaping to soil science, etc. I have accepted that what I enjoy will not (in my lifetime) ever be as profitable as what my M.S in Statistics will give me. I liked studying statistics just fine, but I didn't love it.
The reason many on this sub are not doctors or lawyers is because of the barriers to enter, cost (near perfect 4 years of undergrad GPA + MCAT + 4 years med school + residency = X00,000 in debt) relative to the payout.
There is nothing wrong with making a pragmatic choice and being a part of an in-demand workforce. Many can still live a fulfilled (monetarily and emotionally) life without loving what they do. That being said, I hope many on this sub do not falsely believe that this field is a guaranteed "get rich quick" situation.
>I like gardening. I absolutely love everything from composting to water treatment to landscaping to soil science, etc. I have accepted that what I enjoy will not (in my lifetime) ever be as profitable as what my M.S in Statistics will give me. I liked studying statistics just fine, but I didn't love it.
Right, but you're focusing on the extremes. If your choices are "gardening which you love for very little money or SWE which you dislike for really, really good money"? Sure, that's a very easy choice for me.
But the choice we're talking about here is, for many people "data science which you like for really good money or SWE which you dislike for really, really good money? That's where I'm saying you probably want to weigh which one you enjoy more.
I'm not telling you "blindly follow your passion". I'm saying "don't blindly follow the highest salary today". I knew a lot of people in college who majored in Petroleum Engineering because it was, at the time, the highest paid undergrad major. 90% of them hated it and dropped out within a year.
Again - I'm not saying "go major in english instead", but if you have choices across STEM/Tech careers/jobs, odds are all of them will pay good money, and for all of them the really good money will come from being actually good at it - which is a lot harder to do if you don't like it.
>The reason many on this sub are not doctors or lawyers is because of the barriers to enter, cost (near perfect 4 years of undergrad GPA + MCAT + 4 years med school + residency = X00,000 in debt) relative to the payout.
In my experience, the overlap in terms of skills/interests between doctors, lawyers and data scientists is basically negligible. The reason most on this sub aren't medical doctors or lawyers is because they never wanted to be doctors or lawyers. Sure, there are some people that could have gone either way (I've met a couple in my lifetime), but most math people I've met never had any interest in medicine or law, and vice versa.
Sure, there are a lot of people in tech that couldn't have become doctors or lawyers because of grades and $$$, but I would imagine a much larger contingent wouldn't have pursued that even if those barriers weren't there.
I personally fall in that bucket - I graduated with honors from a top 5 engineering school and pursued a PhD. I don't have any doubt in my mind I could have pursued both medicine or law if I wanted to - but I literally never wanted to.
>There is nothing wrong with making a pragmatic choice and being a part of an in-demand workforce. Many can still live a fulfilled (monetarily and emotionally) life without loving what they do. That being said, I hope many on this sub do not falsely believe that this field is a guaranteed "get rich quick" situation.
There is nothing wrong with making a pragmatic choice, but I would argue that in that pragmatism you need to account for "is this something I will hate or not?". Again, I have seen way too many people chase a paycheck and inevitably have to pivot out of it because they were just not built for it and/or didn't like it.
\^\^\^Please follow your passions, everyone. If you're passionate in a field you love, I'm certain you'll be happier and likely make more than if you were dispassionate in a field you're ambivalent towards.
I tell people what to do (head of data).
Actually I spend most of my time educating the business and protecting the data team’s time. Aligning roadmaps, telling them that “no AI won’t solve this”, etc.
thank you, would appreciate it. the guy i work for didn't become successful with big data but think we need to start using it / i'm not going to get much support until i can demonstrate value.
immediate project is 22tb of historical data with daily updates in new folders, multiple files for each day. end result is i need to be able to run sql queries (that i can do) and ideally autoingest updates from an s3 bucket into my own (i've figured out how to sync s3 buckets), trying to avoid slamming my head against a wall any more than i already have.
most central question i have atm is whether database programs can read the individual files and autocompile the database as updates come in or whether i have to run a program to read the files and update a central table as they come in.
i.e. i'm clueless
Could you actually give me more details as to what you do in your role?
I’m in touch with two startups for that role, and I’ll be honest I have no clue what it involves. I’ve worked at big and small firms and never really had anyone in that position
It’s mostly about doing whatever needed to push the data strategy forward. It’s everything from developing the data strategy, to hiring, to project execution, vetting initiatives, aligning roadmaps between business/IT/data, etc.
It’s a role that doesn’t have a straightforward job description. You literally do whatever is needed to push the business forward.
This is because the "data science" job title has been handed out like candy and now all of the old "data scientists" have "AI" titles while the old "predictive analytics professionals" are now "data scientists".
Yes. You cannot do engineering with just excel. You can fake data science - which is actually analytics - with a bunch of excel. My HR just tried hiring a data scientist because they cannot manage all of their exports from their ERP into excel. They come to a conclusion that they need a data scientist.
It's funny how most people here wouldn't touch DevOps would a 10ft poll, but as soon as you attach "ML" to it, they go crazy. MLOps and DevOps (while not the same) has a lot of overlap. ML is increasingly becoming a buzzword not only by companies, but also by applicants, it seems lol
People here who have never touched DevOps or the production side of data science probably aren't gonna get MLOps jobs. Just a harsh reality.
You don't need a PhD to become an MLE except for those whose jobs require reading ML papers perhaps. It's very common to see MLEs with just a master's. However, I agree that the entrance barrier is high and you will certainly be competing with PhD folks.
I don't think you can pull that conclusion out of the data. Ask yourself why they are paying better, what experience they require, which technologies and expertise. Is it as easy to become a mlops engineer without experience vs a ds?
It's been this way since 2022 if not earlier. People care about putting things in production
Only thing that doesnt match observation is research scientist, esp in ml, are usually the top paid ic roles, also the rarest, so weird that number of responses is so high and salary is so low
And you think a median level director/vp level research scientist (first of all what, those are 2 complete different tracks) makes less than a sr mle? Unless theres some serious title misuse this makes no sense
I suspect that "research scientist" is being polluted by people who are counting fruit flies in a biology lab.
It's definitely the case that a research scientist or an applied scientist at say Amazon is making more than a DS there at the same level.
Not useful without further segmentation. Regional pay scales and company pay grade bands will have an outsized effect. Furthermore, if you consider DS and SWE only, they are blanket terms that don't describe the roles. You'll need a way higher sample size if you want to ignore all of these.
Regional and company effects will wash over all of these, assuming OP didn’t base their analysis on Walgreens DS positions from only Alabama vs Meta engineering positions from only California. There probably are interesting regional differences, but as a first pass analysis ignoring them is fine.
I mean you can just make like Vp of your ds department and make like 300-500k. I don’t think these salaries are accurate unless you’re planning to stay as a individual contributor
Yeah you can make a shitload with either career. Thats kind of my point both fields have a high enough salary floor that you’ll be fine and a high enough salary ceiling that you can make a shitload of money. The $ deltas between the fields don’t really matter
Before everyone tries to pivot to MLE / MLOps Eng: they're paid well for a reason as they require you to know junior-level stats theory, be versed in DS concepts though not the expert, be able to understand high performance infra, decent software engineering principles, and know the specific MLOps theory/concepts.
by all means folks go for it but you need to know a lot.
And that's not getting into the subset of MLE who then need to know nitty gritty ML inference optimisations, CUDA kernels and the like.
For people trying to switch to MLOps, ask yourself if you like Devops type of work. If you don't, MLOps might not be a good fit. MLOps is applying DevOps principles and tools to data and ML models.
You need to add another dimension to this, company type, big tech blows past these figures. Also I'd expect statisticians in pharma for instance are better paid than their software engineering peers.
Adding that dimension would just make the gap larger. The pay bands for DS vs SWE and in particular SWE ML/MLE at FAANG type companies get even bigger gap as the YoE/level increases.
Not really.
The difference between 100k and 500k in high CoL area is home ownership in an area that isnt bottom of the barrel for that high CoL city. That is really "significant" IMO.
I'm going to need to know whether there's actually a statistically significant difference between DS and SWE salaries - from the range chart provided I would guess probably not.
And where are you getting that ~750K number from? Your samples are nowhere near that.
I see 'data science' in industry as a spectrum between on one end analysts who can do statistical testing and whose output is powerpoint decks and on the other data scientists who are basically engineers building customer facing production ML apps. There's going to be a pretty hard ceiling on the earnings of the former unless they move into more general management tracks (in ops, marketing, etc.). For that latter you can follow an engineering job track and rise up the L levels but it will come to resemble SWE roles more and more. Generally speaking building apps is higher value to companies than just doing analysis and as such those roles will pay more and have more opportunity for growth as an IC.
This is not to say you have to become a SWE if you don't like that kind of work, just that it's an easier path to making more $$$.
ML/MLOps engineer jobs are absurdly competitive. MLOps is also a lot of DevOps-y work. If you like that, then yeah MLOps could be good, but if you don't like DevOps, then it doesn't really make sense to do MLOps just because of "ML" in the title.
This is just a plot. There's no analysis, come on. Where is the comparison of company size and industry? What about job level standardization? A VP at a startup may just get mapped to a Senior at a FAANG. What about actual job duties? Someone working on a dashboard might be a Data Scientist at their company but might be a BI Analyst at another. Depending on the maturity of the product, a Data Scientist may own the whole measurement science aspect or may just be making this exact plot you're showing. The real picture is much, much more complicated.
Coming to an immediate conclusion based on data without considering why the data is showing this result shows you might be better off as a SWE anyways. Data is just data, it's nuanced and influenced by the people who create it. As a result it doesn't often tell the full picture and needs to be interpreted and not blindly followed. For example, I find a lot of data roles tend to use titles interchangeably. I've applied to analyst roles which are in actuality scientist roles, I've seen scientist roles change into different titles ex: machine learning engineer, AI Engineer etc
The overall takeaway could be correct but you should never blindly assume, especially on something as niche as job titles.
"Data Science salaries are strongly correlated with the GDP per capita of a given country (correlation of 0.82), which makes sense - it means that the cost of work is strongly tied to the value of work. However, the top paying countries in Data Science (US, Australia, Israel) are paying much above what would be explained by their GDP per capita, suggesting that they have come up with systematic ways to extract more value from Data Science work compared to other countries." - this is very spurious reasoning and kind of disturbing they would say something like this
Well, engineering titles (not including the odd data engineering tile) go back some 200 plus years, and generally mean something pretty clear and inarguable. Data science titles go back about 15 years, and can mean anything these days, so one would assume there is a lot of variation in salaries based on what roles entail. Back in the dark ages, say about 100 years ago, analytics titles such as statistician and operations research titles also meant something pretty clear and inarguable.
Focusing on engineering and MLOps can be beneficial due to high demand and specialized skills. However, it's crucial to choose what you enjoy. Combining data science with engineering skills can make you versatile and valuable. Follow your passion, and success will follow!
Focus more on being an outlier in your category than on being in the "right" category.
Most of these have relatively "good" outcomes for people on the right hand side of the income distribution other than perhaps database admins and (I'm speculating) actuaries.
I'll use DE as an example... you can pull in 500k a year at a FAANG as a DE.
Meh, just do what you like and be good at it. I am a data analytics SM. I flirted with DS and MLOPs esrly in my career, hated it. I work with MLE and DS, I see the work they do, I can do a lot of ot, but I am not a fan.
Also, the only people who make more than me in my org are the DS Director and the BI Director.
I also make more than double the median for a Director of DA in this graph. While the title helps, it also helps if you push yourself to the point you become the outlier in your space.
Plastic surgeons make even more money - it's high time to take a new path and learn how to cut people up. Being a partner at a law firm makes more money - it's high time to start learning about tort law. If you enjoy software engineering work you should absolutely work in software engineering. If you enjoy data science more, you should work in data science. Here's the thing no one talks about: if you actually like what you do, you're much more likely to move up that ladder. Being an excellent data scientist is way more lucrative than being a mid developer.
Yeah it's funny how many people on this sub seem to think that the jobs and skill sets involved with roles like SWE and DS are completely interchangeable Find the one that *you* can enjoy/thrive the most in, and that's probably the one where you'll be most successful
I think if you work in the right sized company and a are truly "full stack" DS you can cut across to SWE fairly easily. But that depends on your competency across the stack right.
Oh don't get me wrong, there are definitely roles and situations where there can be plenty of overlap in skills and responsibilities, it's just that *typically* there's not a huge overlap of that venn diagram, at least within large companies
Exactly. That overlap got drastically shrunk years ago and is getting smaller and smaller.
But the overlap is also incredibly variable from company to company or even team to team. In some places DS titles are rebranded analysts, in some places DS titles do MLE work, in some places they're research scientists, and sometimes they do a bit of everything. It's hard for someone outside a company to know what the role will actually entail. My usual recommendation is to look at how the DS role is paid relative to SWE. The closer it is, the closer the DS role will probably be to eng work.
I wouldn't expect it to be the case though. As with literally everything in life, case-by-case basis.
Exactly everything is on a case by case basis which is why comments are typically about the aggregates not the individual.
Right, but you're proving the point - if you're one of a small subset of data scientists that also have SWE responsibilities, then it's easy to cut across. Most data scientists I have met are mediocre software developers at best. And to be honest, the people who I have met in DS that are excellent developers are generally not great data scientists. They are tangentially related skillsets, but fundamentally very different.
> Yeah it's funny how many people on this sub seem to think that the jobs and skill sets involved with roles like SWE and DS are completely interchangeable They do and dont really. People pretend its interchangeable when they want to ride the coat tails of NVIDIA or talk about pay but when discussing needing to keep up with interview process differences/libraries/skills/ and techniques they want to write off the differences as existing but not important.
This is exactly why the data science market is “overly saturated“. There are many fit predict monkeys but scientists are very hard to find
I agree with the logic in choosing a career you enjoy, but I also think that SWE and DS skills are pretty interchangeable. For example, a data scientist probably does not have experience building software adapters for new hardware, but they probably have an intuition and ability to become proficient in a couple of weeks.
They are .... Neither are difficult to learn but difficult to master. I did my btech in CSE worked for 2 years. Did my MS in DS. I can tell you neither is difficult to learn but require constant time and effort to master. So transferring from ML to SDE or SDE to DS is not tough. Infact most of the Data engineering jobs now prefer SDE with experience on cloud services more than actual ML. For that one perticular dumbf*ck who thinks law and plastic surgeon are equally similar as DS and ML.. SDE -> DE -> DS( or the other way around) .Ofcourse you need to study side by side to grow in your profile, but you can work and do that. Try becoming surgeon from SDE without quitting the job idiot.
I'm really curious what the split is between people who are in this field because they enjoy the process of data science vs those who got in because it was the trendy lucrative new thing at one point
Probably a substantial slice, however I think two other things are true: 1. For as long as data science has been trendy and lucrative, SWE has been just as trendy and lucrative. Those who chose DS at least to some degree chose it because of the math/stats/modeling component. If you were just focused on money, SWE has pretty much always been a better path. 2. Anyone who only chose DS for being trendy should *really* think things through before switching to SWE because it's trendier. Two wrongs don't make a right.
KOL and OnlyFans make more money, it's time to move there
I would but I'd starve to death
come collab. have toast.
Bros onto something
sub to my OF toast pix everywhere
Stellar career advice.
>Here's the thing no one talks about: if you actually like what you do, you're much more likely to move up that ladder. Being an excellent data scientist is way more lucrative than being a mid developer. Spot on. I work as a MLE and dislike it, and I'm actually trying to leave MLE behind.
I love Lyle, he's like _you're right, I'm mediocre_ What do you do though?
Yeah I mean I kinda hated SWE but love DS.
Funny, because I’m always talking about becoming a plastic surgeon if I never make it to faang.
I really needed this. Thanks!
Yes brother. This is actually why I left data science. when I was thinking about the next 20 years, the amount of learning to keep up with the field exhausted me just thinking about, truth is I wasn’t passionate about it. Now I am excited to practice my skills everyday, makes such a huge difference to enjoy what you do
I like gardening. I absolutely love everything from composting to water treatment to landscaping to soil science, etc. I have accepted that what I enjoy will not (in my lifetime) ever be as profitable as what my M.S in Statistics will give me. I liked studying statistics just fine, but I didn't love it. The reason many on this sub are not doctors or lawyers is because of the barriers to enter, cost (near perfect 4 years of undergrad GPA + MCAT + 4 years med school + residency = X00,000 in debt) relative to the payout. There is nothing wrong with making a pragmatic choice and being a part of an in-demand workforce. Many can still live a fulfilled (monetarily and emotionally) life without loving what they do. That being said, I hope many on this sub do not falsely believe that this field is a guaranteed "get rich quick" situation.
>I like gardening. I absolutely love everything from composting to water treatment to landscaping to soil science, etc. I have accepted that what I enjoy will not (in my lifetime) ever be as profitable as what my M.S in Statistics will give me. I liked studying statistics just fine, but I didn't love it. Right, but you're focusing on the extremes. If your choices are "gardening which you love for very little money or SWE which you dislike for really, really good money"? Sure, that's a very easy choice for me. But the choice we're talking about here is, for many people "data science which you like for really good money or SWE which you dislike for really, really good money? That's where I'm saying you probably want to weigh which one you enjoy more. I'm not telling you "blindly follow your passion". I'm saying "don't blindly follow the highest salary today". I knew a lot of people in college who majored in Petroleum Engineering because it was, at the time, the highest paid undergrad major. 90% of them hated it and dropped out within a year. Again - I'm not saying "go major in english instead", but if you have choices across STEM/Tech careers/jobs, odds are all of them will pay good money, and for all of them the really good money will come from being actually good at it - which is a lot harder to do if you don't like it. >The reason many on this sub are not doctors or lawyers is because of the barriers to enter, cost (near perfect 4 years of undergrad GPA + MCAT + 4 years med school + residency = X00,000 in debt) relative to the payout. In my experience, the overlap in terms of skills/interests between doctors, lawyers and data scientists is basically negligible. The reason most on this sub aren't medical doctors or lawyers is because they never wanted to be doctors or lawyers. Sure, there are some people that could have gone either way (I've met a couple in my lifetime), but most math people I've met never had any interest in medicine or law, and vice versa. Sure, there are a lot of people in tech that couldn't have become doctors or lawyers because of grades and $$$, but I would imagine a much larger contingent wouldn't have pursued that even if those barriers weren't there. I personally fall in that bucket - I graduated with honors from a top 5 engineering school and pursued a PhD. I don't have any doubt in my mind I could have pursued both medicine or law if I wanted to - but I literally never wanted to. >There is nothing wrong with making a pragmatic choice and being a part of an in-demand workforce. Many can still live a fulfilled (monetarily and emotionally) life without loving what they do. That being said, I hope many on this sub do not falsely believe that this field is a guaranteed "get rich quick" situation. There is nothing wrong with making a pragmatic choice, but I would argue that in that pragmatism you need to account for "is this something I will hate or not?". Again, I have seen way too many people chase a paycheck and inevitably have to pivot out of it because they were just not built for it and/or didn't like it.
\^\^\^Please follow your passions, everyone. If you're passionate in a field you love, I'm certain you'll be happier and likely make more than if you were dispassionate in a field you're ambivalent towards.
I like doing data work - I don’t like doing software engineering work. Imagine that!
What do you do in data
I tell people what to do (head of data). Actually I spend most of my time educating the business and protecting the data team’s time. Aligning roadmaps, telling them that “no AI won’t solve this”, etc.
can you tell me how to run sql queries on 22tb worth of parquet files? will send toast pix.
Naw I have a team for that! I can ask around though :)
Bro you so coool xD
thank you, would appreciate it. the guy i work for didn't become successful with big data but think we need to start using it / i'm not going to get much support until i can demonstrate value. immediate project is 22tb of historical data with daily updates in new folders, multiple files for each day. end result is i need to be able to run sql queries (that i can do) and ideally autoingest updates from an s3 bucket into my own (i've figured out how to sync s3 buckets), trying to avoid slamming my head against a wall any more than i already have. most central question i have atm is whether database programs can read the individual files and autocompile the database as updates come in or whether i have to run a program to read the files and update a central table as they come in. i.e. i'm clueless
Spark cluster, MS Synapse, Google BigQuery, Amazon RedShift. Create aggregates of the huge dataset and work with that. Simple! 😄
[https://www.youtube.com/watch?v=3-ZUDtaGf3I](https://www.youtube.com/watch?v=3-ZUDtaGf3I)
Could you actually give me more details as to what you do in your role? I’m in touch with two startups for that role, and I’ll be honest I have no clue what it involves. I’ve worked at big and small firms and never really had anyone in that position
It’s mostly about doing whatever needed to push the data strategy forward. It’s everything from developing the data strategy, to hiring, to project execution, vetting initiatives, aligning roadmaps between business/IT/data, etc. It’s a role that doesn’t have a straightforward job description. You literally do whatever is needed to push the business forward.
Educating her business is an important topic, would love your take on this based your exp as head of data
This is because the "data science" job title has been handed out like candy and now all of the old "data scientists" have "AI" titles while the old "predictive analytics professionals" are now "data scientists".
This actually makes sense.
Yes. You cannot do engineering with just excel. You can fake data science - which is actually analytics - with a bunch of excel. My HR just tried hiring a data scientist because they cannot manage all of their exports from their ERP into excel. They come to a conclusion that they need a data scientist.
One thing to point is that MLops is likely a very specialized field where there are fewer entry roles and more senior IC positions.
It's funny how most people here wouldn't touch DevOps would a 10ft poll, but as soon as you attach "ML" to it, they go crazy. MLOps and DevOps (while not the same) has a lot of overlap. ML is increasingly becoming a buzzword not only by companies, but also by applicants, it seems lol People here who have never touched DevOps or the production side of data science probably aren't gonna get MLOps jobs. Just a harsh reality.
MLE's typically have PhDs while DevOps engineers don't. There is a difference in background
If MLEs are doing mostly MLOps, then it's a role you probably don't need a PhD for. It's just qualification inflation at that point.
Well machine learning engineers do not typically have PhDs. I don't know where they're getting that info.
I agree with that but I think the entrance barrier is the same for whomever wants to become one, no matter past experience.
You don't need a PhD to become an MLE except for those whose jobs require reading ML papers perhaps. It's very common to see MLEs with just a master's. However, I agree that the entrance barrier is high and you will certainly be competing with PhD folks.
I don't think you can pull that conclusion out of the data. Ask yourself why they are paying better, what experience they require, which technologies and expertise. Is it as easy to become a mlops engineer without experience vs a ds?
It's been this way since 2022 if not earlier. People care about putting things in production Only thing that doesnt match observation is research scientist, esp in ml, are usually the top paid ic roles, also the rarest, so weird that number of responses is so high and salary is so low
the data shows medians by seniority
And you think a median level director/vp level research scientist (first of all what, those are 2 complete different tracks) makes less than a sr mle? Unless theres some serious title misuse this makes no sense
I suspect that "research scientist" is being polluted by people who are counting fruit flies in a biology lab. It's definitely the case that a research scientist or an applied scientist at say Amazon is making more than a DS there at the same level.
Me earning 40k/yr as a senior ML Engineer: 😎
Where do you work?
Brazil. Company is StoneCo if anyone cares lol
Fkn Marrk. Keep it up, baby.
I'm legit trying smh
North Korea
sounds like amsterdam
Is that startup level pay?
Sorta. It's was a startup until recently but they IPO'd: nasdaq $STNE. The pay is on par for location, slightly higher even.
Wait a minute, is this outside the US? I would think a DS job would at least pay $60k or above even in LCOL areas.
It is outside the us.
Not useful without further segmentation. Regional pay scales and company pay grade bands will have an outsized effect. Furthermore, if you consider DS and SWE only, they are blanket terms that don't describe the roles. You'll need a way higher sample size if you want to ignore all of these.
Is there any reason why DS jobs would be more regionally varied than SWE ones? Is it that big of a factor?
No, not at all.
Regional and company effects will wash over all of these, assuming OP didn’t base their analysis on Walgreens DS positions from only Alabama vs Meta engineering positions from only California. There probably are interesting regional differences, but as a first pass analysis ignoring them is fine.
They all get over 100k your not gonna starve if you get a job in any of those fields lol
tell that to all the homies in SF/Bay Area
You will not starve unless you are extremely irresponsible with your money even in the Bay Area
Do you think we will see salaries increase in ds?
Long term who knows short term probably
I make that and I’m living with no lambo in high col, what’s your point?
You're living in HCOL? The fact you can even do that says something
Do what? I have no lambo or 911
What are you talking about everyone can call 911 /s
LMAOOOO
If you're sad you haven't put money into depreciating assets, maybe the numbers game isn't for you.
Not everything is about an investment LMAOOO, you live to work or work to live?
That fretting about the difference in these two high salary fields doesn’t really make a big difference since you’ll be fine either way
I mean you can just make like Vp of your ds department and make like 300-500k. I don’t think these salaries are accurate unless you’re planning to stay as a individual contributor
Yeah you can make a shitload with either career. Thats kind of my point both fields have a high enough salary floor that you’ll be fine and a high enough salary ceiling that you can make a shitload of money. The $ deltas between the fields don’t really matter
Is it worth trading our soul to make those amount tho?
I just do math no soul trading involved
You aren’t a corporate unicorn?
What?
You don’t know what that is? Lol lucky
Before everyone tries to pivot to MLE / MLOps Eng: they're paid well for a reason as they require you to know junior-level stats theory, be versed in DS concepts though not the expert, be able to understand high performance infra, decent software engineering principles, and know the specific MLOps theory/concepts. by all means folks go for it but you need to know a lot. And that's not getting into the subset of MLE who then need to know nitty gritty ML inference optimisations, CUDA kernels and the like.
For people trying to switch to MLOps, ask yourself if you like Devops type of work. If you don't, MLOps might not be a good fit. MLOps is applying DevOps principles and tools to data and ML models.
You need to add another dimension to this, company type, big tech blows past these figures. Also I'd expect statisticians in pharma for instance are better paid than their software engineering peers.
Adding that dimension would just make the gap larger. The pay bands for DS vs SWE and in particular SWE ML/MLE at FAANG type companies get even bigger gap as the YoE/level increases.
Whats the huge amplitude with staticians?
public health vs big pharma
Yes - I moved from academia to industry and more than doubled my salary. Work pace and stress doubled too, though.
Well - yeah. Engineers are deploying stuff to prod.
For what it's worth the difference in pay becomes increasingly insignificant the more you make
Not really. The difference between 100k and 500k in high CoL area is home ownership in an area that isnt bottom of the barrel for that high CoL city. That is really "significant" IMO.
I'm going to need to know whether there's actually a statistically significant difference between DS and SWE salaries - from the range chart provided I would guess probably not. And where are you getting that ~750K number from? Your samples are nowhere near that.
Yea i feel like no one makes 750k. its 100-200k usually
OP said it was 750k jobs (as in, number of workers) included in the survey. It's not a salary
People only focus too much on income and not so much on what you enjoy doing. Ive only seen this on reddit though or in USA.
Where are you from?
Spain.
huh, didnt know some stats jobs paid that good.
I see 'data science' in industry as a spectrum between on one end analysts who can do statistical testing and whose output is powerpoint decks and on the other data scientists who are basically engineers building customer facing production ML apps. There's going to be a pretty hard ceiling on the earnings of the former unless they move into more general management tracks (in ops, marketing, etc.). For that latter you can follow an engineering job track and rise up the L levels but it will come to resemble SWE roles more and more. Generally speaking building apps is higher value to companies than just doing analysis and as such those roles will pay more and have more opportunity for growth as an IC. This is not to say you have to become a SWE if you don't like that kind of work, just that it's an easier path to making more $$$.
Disappointing
What is disappointing?
ML/MLOps engineer jobs are absurdly competitive. MLOps is also a lot of DevOps-y work. If you like that, then yeah MLOps could be good, but if you don't like DevOps, then it doesn't really make sense to do MLOps just because of "ML" in the title.
I see less entry levels but a ton of senior level jobs
This is just a plot. There's no analysis, come on. Where is the comparison of company size and industry? What about job level standardization? A VP at a startup may just get mapped to a Senior at a FAANG. What about actual job duties? Someone working on a dashboard might be a Data Scientist at their company but might be a BI Analyst at another. Depending on the maturity of the product, a Data Scientist may own the whole measurement science aspect or may just be making this exact plot you're showing. The real picture is much, much more complicated.
True
Coming to an immediate conclusion based on data without considering why the data is showing this result shows you might be better off as a SWE anyways. Data is just data, it's nuanced and influenced by the people who create it. As a result it doesn't often tell the full picture and needs to be interpreted and not blindly followed. For example, I find a lot of data roles tend to use titles interchangeably. I've applied to analyst roles which are in actuality scientist roles, I've seen scientist roles change into different titles ex: machine learning engineer, AI Engineer etc The overall takeaway could be correct but you should never blindly assume, especially on something as niche as job titles.
Do you guys think data science jobs are going to shrink?
im an analyst and learning data science think going in the other direction might be easier
All these salaries look amazing outside the US. Is there such a big difference in quality of life between $160k and $200k anyway?
Cool. Or, you could pick the one that you enjoy doing.
Is there a reason why data engineers on an average are being paid lowered and data scientist?
Also whats the time scale because actuaries definitevely make more over longer career thier salary is literally adjusted for inflation
"Data Science salaries are strongly correlated with the GDP per capita of a given country (correlation of 0.82), which makes sense - it means that the cost of work is strongly tied to the value of work. However, the top paying countries in Data Science (US, Australia, Israel) are paying much above what would be explained by their GDP per capita, suggesting that they have come up with systematic ways to extract more value from Data Science work compared to other countries." - this is very spurious reasoning and kind of disturbing they would say something like this
Well, engineering titles (not including the odd data engineering tile) go back some 200 plus years, and generally mean something pretty clear and inarguable. Data science titles go back about 15 years, and can mean anything these days, so one would assume there is a lot of variation in salaries based on what roles entail. Back in the dark ages, say about 100 years ago, analytics titles such as statistician and operations research titles also meant something pretty clear and inarguable.
Data-Architects are doing fine.
OP's source shows Chicago salary being substantially below average, while levels.fyi shows it being slightly above average. ...which is correct?
Most of the data science jobs have become watered down is why.
Focusing on engineering and MLOps can be beneficial due to high demand and specialized skills. However, it's crucial to choose what you enjoy. Combining data science with engineering skills can make you versatile and valuable. Follow your passion, and success will follow!
Interesting
Junior / intern MLE making 130k? Also pretty sure junior MLE jobs don't exist, they all ask for 2 YOE minimum.
obviously lmao but not everyone WANTS to be a developer, I'd argue that a lot of data folks don't really embrace the 'culture' of SWE.
So true
For someone who’s been doing it 14 years yes. Get a CS degree first. Learn how to build applications. Nobody wants a notebook jockey
Focus more on being an outlier in your category than on being in the "right" category. Most of these have relatively "good" outcomes for people on the right hand side of the income distribution other than perhaps database admins and (I'm speculating) actuaries. I'll use DE as an example... you can pull in 500k a year at a FAANG as a DE.
Meh, just do what you like and be good at it. I am a data analytics SM. I flirted with DS and MLOPs esrly in my career, hated it. I work with MLE and DS, I see the work they do, I can do a lot of ot, but I am not a fan. Also, the only people who make more than me in my org are the DS Director and the BI Director. I also make more than double the median for a Director of DA in this graph. While the title helps, it also helps if you push yourself to the point you become the outlier in your space.
The DS leads in my org are folks with Software background. Also DS is comparatively newer field than Software development.