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.
(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?
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.
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.
True, but those software engineers exist, and FAANG and FAANG-adjacent companies offer the work opportunities, prestige and salaries to attract those "twofer" engineers.
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
"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
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.
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.
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.
Yes, and there is zero prestige within company, awful lot of high-pressure work, no control over bugs in infrastructure pieces that tend to blow up unexpectedly in production, getting stuck on a certain stack that tends to age badly (who remembers Hadoop or Spark 1.3 these days?), making one quickly replaceable by younger folks with the latest hyped tech, small understanding of the data science part, no joy from constructing great algorithms either. It's a needed job of course; it might pay well at this very moment but it's a perfect target for automation once latest cognitive automation research bubbles down to production.
If you want to shine, do Deep Learning (research if possible), low-level distributed systems you have control of as an author of an important piece of infrastructure or the actual data modeling and predictions.
He's right about how data pipeline work is regarded by most ML scientists. It often ends up being extremely hard work and is regarded by others as useless grunt work. There is no big reward, constant overtime due to unexpected issues and lots of stress.
SQL skills are expected of every data scientist and backend engineer, but are also found in a much larger and cheaper labor pool. As far as I can tell, the data engineering role exists to take advantage of those economics.
ETL pipeline queries and configs are absolutely critical, make or break an ML project, and account for most of its labor hours. But they are the most commoditized part of it.
I love data engineering - atleast when I practiced it at an adtech company, it involved lots of interesting challenges around performance, distributed systems and a mix of SWE & Ops work. Buut, applying to data engineering jobs I see where the parent poster is coming from (minus the condescension) - a lot of them are about writing simple ETL jobs, and they tend to be lower paid & less prestigious.
Those certainly exist, but aren’t what I was referring to in my post. The “data engineers” I’ve worked with (and am) are usually distributed systems people who also know a bit of data science. In that case it’s basically a higher “prestige” software engineering role
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.
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.
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.
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.
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).
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.
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?
What exactly are the qualifications? I would have thought a serious, sustained study of statistics--starting with a strong base knowledge of the mathematics of probability and building from there. But based on the resumes I've seen that doesn't seem to be the common opinion.
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.
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.
