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Data Engineer will be the most in-demand role as Data Science becomes interactively more simple to perform (I.e. AutoML)
ML is just one of the many tools a data scientist uses. Often ML isn’t the answer. It’s more about the process of continuous learning and feedback, not so much about the techniques or tools. Auto ML however is great for a quick and dirty poc.
Don’t take this the wrong way but this is a pretty stupid question
They are all well paid and in demand roles but while they have overlapping responsibilities, the focus of the work is different. You should look into trying the different fields to find a good fit for you, rather than looking for some marginal edge in remuneration, if you don’t know which you like/should go for.
A data scientist is the more business minded person of the 3 while DE is in the middle and an MLE is focused on ml
I'd say it's between a DS and ML
Data science job was over rated and many employers are realizing it with the work needed in the data engineering side which was a missing skill in many of data scientists. Become a ML engineer who can code, do feature engineering and do the data engineering.
I have a stats background but wish I could code better. From my perspective, you’re probably more employable as an engineer than a data scientist.
People are totally ok with deploying auto-ml projects and cookie cutter AB testing. I’ve rarely been on projects that went long enough to justify squeezing some extra gains for a model or really putting everything through the ringer to test outputs.
Apple just had a huge scandal for gender bias in it’s credit models. I’m sure they had people that could have caught it but probably understaffed it and didn’t give people enough time to check it. If a company as big as Apple isn’t really investing in data science talent I don’t think it bodes well for everyone else.
I agree with your first paragraph but I’m not sure this is the same case with the Apple Card.
I don’t know if you can presume that one guy going off on Twitter is evidence of systemic bias.
Also consider what you want to do after consulting.
I'm a former DE and exited to an industry that doesn't use ml so I'm glad I didn't waste time trying to learn that stuff. Having a bit of experience doing statistical analysis is always helpful as well
Repeating what most people already said here:
DS - as DS becomes commoditized, it will be much easier to access and pay for easy to use, quick tools,
MLE - If youre doing research into development of new models (usually need math/stats background) this might be a good
option
DE - IMHO it’s the best choice given that companies will ALWAYS need a data infrastructure.
MLE. DS is a bloated title now. Take time to learn data engineering.
I sometimes wish this bowl and conversation didn’t exist. People used to leave me alone when I was a “statistician”, but now as a “data scientist”, I have 3 dozen money hungry kids asking me how to trick people into thinking they’re “Silicon Valley” grade coders, as if that’s even a thingn
I’m currently a data scientist, thinking of transitioning into ML Engineering
May I ask why? I'm currently a ML Engineer looking to become a Data Scientist.
Can't go wrong with DE or MLE. I feel sometimes the line is blurred between the two.
Also curious ^
They’re just titles to align yourself to trends and can mean different things different places.
Typically a data science project involves a modeler who supplies business rules to an engineer who executes the logic. There’s also a PM who aligns the project with sponsors. Sometimes the modeler is a data analyst, sometimes they’re an MLE or a DE. Sometimes the engineer is an MLE, DE or SE. Title doesn’t really matter, they’re all paid well. Just do good work so you can work on more interesting projects that allow you to grow the skills you enjoy.
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