5 ms·

Compensation may be going up, but at top tech companies, software engineers still make significantly more than data scientists for the same level (twice the RSU

Compensation may be going up, but at top tech companies, software engineers still make significantly more than data scientists for the same level (twice the RSUs). I’ve noticed at non-tech companies, the reverse seems to be true: data scientists make slightly more than software engineers.

ptd 8y ago
How do Machine Learning engineers stack up? Or Data Scientists who work in ML, same outcome?
- ummonk 8y ago
  Machine learning engineers (i.e. people with graduate degrees, preferably PhDs) typically outearn both, though when you factor in the work experience that software engineers have accumulated during the time the machine learning people were pursuing the PhD, it's pretty comparable. It's unclear how long this will last now that everyone is pursuing grad degrees in ML/AI. Data scientists who do ML don't have the same prestige (and pay) as those who have that ML stamp from universities.
  - ACow_Adonis 8y ago
    Obviously I'm biased in the other direction (and in another country), but this is interesting to me. I've always taken the ML stamp from universities as a visible sign that you're a newbie, if not purely because those qualifications haven't been around for a sufficiently long time, so its basically a signal when you've got one that you must be just starting out.
opportune 8y ago
That’s why you become a data engineer and get the best of both worlds. If you can market yourself to a company as a “full stack data developer” you will make more money than either category
- amznthrowaway2 8y ago
  "data engineer" is often considered a lower prestige title than data scientist. "Data scientist" is a title recently thrown around a lot for positions that used to be called "data analyst", with no strong ML or SWE ability required. Amazon's title for scientists with strong ML engineering ability is called "Applied Scientist" and it's paid significantly higher than SWE
  - grogenaut 8y ago
    You're claiming to know amazon internal except you're also using SWE, not SDE, the standard name for software devs there. Also amazon seems to hire very few Applied Scientists and thus is likely cherry picking for this role. The one I work near is an industry thought leader at Principal Applied Scientist. They'd be a leading researcher if they were in academia.
    - amznthrowaway2 8y ago
      Software engineers are given the SDE title at Amazon but I just used SWE as the more general "software engineer" acronym.
  - gaius 8y ago
    a title recently thrown around a lot for positions that used to be called "data analyst" Agreed. Maybe this is different in different markets but in London the vast majority of "data scientist" positions advertised are really PowerBI, Excel, etc. If there is any ML it is just to feed data into a black box model. If you were both smart and lucky you might be able to sneak R in by the backdoor and start doing actual data science, but it would be an uphill battle.
- bitL 8y ago
  Data Engineer role is about building big data/streaming pipelines for data and often DevOps aspects of keeping such pipeline deployed and online. A boring, dead-end job. Avoid. Make yourself both ML/DL master and SWEng and you'll be doing extremely well.
pbalau 8y ago
Not sure if your reply is on topic. Interesting reply, but the original article didn't state data scientists are making more than swes. The article just states that the demand for data peeps and the benefits they get are growing.
- lsc 8y ago
  It's relevant to nearly everyone who would be interested in the title of the article, though.
  - pbalau 8y ago
    how? if profession A gets more demand and pay, does it matter if profession B gets more or less? Is it that damn important if A makes more than B or if B makes more than A?
    - lsc 8y ago
      I'm assuming most people who are interested in the title are interested as part of the research one does to decide in what direction they ought to advance their career. If I'm choosing to train up for job A or job B, I mean, money certainly isn't the only consideration, but it's certainly a very important consideration.
    - thucydidesofusa 8y ago
      It matters when profession A and profession B are as related as data science / data engineering / software engineering. I'm not arguing that there's no distinction between the fields, just that people who excel in one can usually do a passable job working in the others. Or rather, you have to have passable skills in the other fields to excel at your chosen field.
    - greenhatman 8y ago
      It's relevant for software engineers who read the title and consider going into data science instead.
didibus 8y ago
My experience at big tech is that software engineers kinda do it all. It's almost DevOpsPmDbaDsDeMle. Where Ds is data scientist, De is data engineer and Mle is machine learning engineer. And ML scientists are just SE with PhDs in ML. Basically, they tend to try and not create seperate isolated roles in specialized domains, but instead hire people who can do and figure it all. The opposite seems true in smaller companies. They break things up, try to hire different roles, specialist in different domains, and segregate each one from one another. Obviously, mileage may vary, and I'm doing a massive generalisation of big and small businesses, but this is just my observation.
spamizbad 8y ago
(Disclaimer: generalization ahead) That might be because they're starting to get hip to the fact that too many data scientists struggle to produce professional-level code, forcing you to pay a software developer to create the actual deliverable from their prototype. At that point, why not just hire a really bright software engineer who can code well and knows how to work with data?
- walrus1066 8y ago
  I'm not sure it's easy for a software engineer to develop a model, it's quite an orthogonal skill, you need a solid grasp on mathematics and especially statistics.
  - amitport 8y ago
    It's not easy, but there are capable developers that are proficient in data science and it may be cost effective to hire one such (high cost) programmer than one (average cost) data-scientist and another (average cost) programmer.
  - spamizbad 8y ago
    True, but those software engineers exist, and FAANG and FAANG-adjacent companies offer the work opportunities, prestige and salaries to attract those "twofer" engineers.
tmp092 8y ago
Does Google even have a "Data Scientist" title? I don't think I've ever come across a JD for one.
- higginsc 8y ago
  Yep, they do. I know several and have been through part of the interview process (I withdrew because Google takes too long, and I went to FB instead of dealing with their bullshit).
minimaxir 8y ago
People conflate data scientists, data engineers, and software engineers. Although there is strong overlap between all 3 disciplines, and it benefits to know all 3 disciplines, the day to day responsibilities of all 3 are very different. Startups try to hire people with double/triple disciplines to reduce costs though.

ptd 8y ago

How do Machine Learning engineers stack up? Or Data Scientists who work in ML, same outcome?

ummonk 8y ago
Machine learning engineers (i.e. people with graduate degrees, preferably PhDs) typically outearn both, though when you factor in the work experience that software engineers have accumulated during the time the machine learning people were pursuing the PhD, it's pretty comparable. It's unclear how long this will last now that everyone is pursuing grad degrees in ML/AI. Data scientists who do ML don't have the same prestige (and pay) as those who have that ML stamp from universities.
- ACow_Adonis 8y ago
  Obviously I'm biased in the other direction (and in another country), but this is interesting to me. I've always taken the ML stamp from universities as a visible sign that you're a newbie, if not purely because those qualifications haven't been around for a sufficiently long time, so its basically a signal when you've got one that you must be just starting out.

opportune 8y ago

That’s why you become a data engineer and get the best of both worlds. If you can market yourself to a company as a “full stack data developer” you will make more money than either category

amznthrowaway2 8y ago
"data engineer" is often considered a lower prestige title than data scientist. "Data scientist" is a title recently thrown around a lot for positions that used to be called "data analyst", with no strong ML or SWE ability required. Amazon's title for scientists with strong ML engineering ability is called "Applied Scientist" and it's paid significantly higher than SWE
- grogenaut 8y ago
  You're claiming to know amazon internal except you're also using SWE, not SDE, the standard name for software devs there. Also amazon seems to hire very few Applied Scientists and thus is likely cherry picking for this role. The one I work near is an industry thought leader at Principal Applied Scientist. They'd be a leading researcher if they were in academia.
  - amznthrowaway2 8y ago
    Software engineers are given the SDE title at Amazon but I just used SWE as the more general "software engineer" acronym.
- gaius 8y ago
  a title recently thrown around a lot for positions that used to be called "data analyst" Agreed. Maybe this is different in different markets but in London the vast majority of "data scientist" positions advertised are really PowerBI, Excel, etc. If there is any ML it is just to feed data into a black box model. If you were both smart and lucky you might be able to sneak R in by the backdoor and start doing actual data science, but it would be an uphill battle.
bitL 8y ago
Data Engineer role is about building big data/streaming pipelines for data and often DevOps aspects of keeping such pipeline deployed and online. A boring, dead-end job. Avoid. Make yourself both ML/DL master and SWEng and you'll be doing extremely well.

pbalau 8y ago

Not sure if your reply is on topic. Interesting reply, but the original article didn't state data scientists are making more than swes. The article just states that the demand for data peeps and the benefits they get are growing.

lsc 8y ago
It's relevant to nearly everyone who would be interested in the title of the article, though.
- pbalau 8y ago
  how? if profession A gets more demand and pay, does it matter if profession B gets more or less? Is it that damn important if A makes more than B or if B makes more than A?
  - lsc 8y ago
    I'm assuming most people who are interested in the title are interested as part of the research one does to decide in what direction they ought to advance their career. If I'm choosing to train up for job A or job B, I mean, money certainly isn't the only consideration, but it's certainly a very important consideration.
  - thucydidesofusa 8y ago
    It matters when profession A and profession B are as related as data science / data engineering / software engineering. I'm not arguing that there's no distinction between the fields, just that people who excel in one can usually do a passable job working in the others. Or rather, you have to have passable skills in the other fields to excel at your chosen field.
  - greenhatman 8y ago
    It's relevant for software engineers who read the title and consider going into data science instead.

didibus 8y ago

My experience at big tech is that software engineers kinda do it all. It's almost DevOpsPmDbaDsDeMle. Where Ds is data scientist, De is data engineer and Mle is machine learning engineer. And ML scientists are just SE with PhDs in ML. Basically, they tend to try and not create seperate isolated roles in specialized domains, but instead hire people who can do and figure it all. The opposite seems true in smaller companies. They break things up, try to hire different roles, specialist in different domains, and segregate each one from one another. Obviously, mileage may vary, and I'm doing a massive generalisation of big and small businesses, but this is just my observation.

spamizbad 8y ago

(Disclaimer: generalization ahead) That might be because they're starting to get hip to the fact that too many data scientists struggle to produce professional-level code, forcing you to pay a software developer to create the actual deliverable from their prototype. At that point, why not just hire a really bright software engineer who can code well and knows how to work with data?

walrus1066 8y ago
I'm not sure it's easy for a software engineer to develop a model, it's quite an orthogonal skill, you need a solid grasp on mathematics and especially statistics.
- amitport 8y ago
  It's not easy, but there are capable developers that are proficient in data science and it may be cost effective to hire one such (high cost) programmer than one (average cost) data-scientist and another (average cost) programmer.
- spamizbad 8y ago
  True, but those software engineers exist, and FAANG and FAANG-adjacent companies offer the work opportunities, prestige and salaries to attract those "twofer" engineers.

tmp092 8y ago

Does Google even have a "Data Scientist" title? I don't think I've ever come across a JD for one.

higginsc 8y ago
Yep, they do. I know several and have been through part of the interview process (I withdrew because Google takes too long, and I went to FB instead of dealing with their bullshit).

minimaxir 8y ago

People conflate data scientists, data engineers, and software engineers. Although there is strong overlap between all 3 disciplines, and it benefits to know all 3 disciplines, the day to day responsibilities of all 3 are very different. Startups try to hire people with double/triple disciplines to reduce costs though.