Besides your level of interest in each field, it’s important to note that at the top tech companies, software engineers typically receive 2x the RSUs as data scientists / data engineers for a given level. This is non-negligible and can range from an extra $30k to $100k per year depending on your level (base and bonus are typically the same though). Add in the time opportunity cost of getting a PhD, and a top software engineer can save up considerably more money by age 30 than a data scientist.
Money certainly isn’t everything, but I’m considering switching to software engineering (from data science) because I would like to reach financial independence more quickly than my current trajectory allows.
Data Science is a useful skillset for everyone to have, but the majority of the work in any practical data science role is in getting the data in the right place, in the right format to do the data science. This makes it such that most small companies can't actually support having a full time data scientist who can't also write code.
You have a good background in stats and ML - use that with practical experience in SWE to make your skillset more useful and broadly applicable.
I'm going to take this in a different direction and say: That's not the important question.
The important question is whether you're interested in what the company and specific team are doing. Example: I once interned at Google, on the Chrome team. I mean, it's GOOGLE, free food and wonderful smart people and again it is GOOGLE and I'm an UNDERGRAD! What I learned that summer was that I don't actually care about web browsers at all. And so my internship was kind of a bust just because I didn't care much about what we were doing. I had no drive to stay at the office late to keep grinding away at the problem.
What motivates you? What really interests you? Whether you're doing data science or software engineering, that will be far more important to you having a successful internship.
As someone that did my internship in Data Science, if I were to go back and do it again, I would choose Software Engineering instead. Don't get me wrong, I really liked my position and my team, and loved what I did everyday. Career-wise though, I would consider Software Engineering better, unless you plan on doing a Masters/PHD right after undergrad. Data Science is a much younger field than Software Engineering. Whi…
I am actually planning on doing a PhD right after my undergrad. My issue is with what to do before then.
If you’re set on the (data science, I presume) PhD, I would suggest a SE internship. The rationale is that you’re going to be deep in DS for a few years so this is a good opportunity to explore another field. And what you learn during the internship (how to write clean code and document it, unit tests, version control, seeing production software) will a) put you in good stead for your PhD which presumably will be code-intensive and b) set you apart from the rest of the data science pack once you graduate. I work with (junior / intern, to be fair) data scientists and OMG, bashing together a Jupyter notebook != knowing how to program. How to get from a trained model to production code is in my opinion a vastly underdeveloped topic in data science. Having SE experience will definitely help you see how the other half lives.
Apply, apply, apply, apply! Don't filter yourself out of jobs (Or anything in life, honestly!). Hiring is so hard that people barely know what they need or who will help. You show up, be honest, let them be honest in response, keep moving. You're too young (I don't know your age, but young in the process) to be worried about which is perfect. Apply to both and whichever you get do it super well. Can't lose! Good luck…
Agreed.
Also be sure to explain in a customized cover letter why you would be a good match for each position you apply for.
Build a model to predict which positions you have more chances to pass.
Build an algorithm to automatically apply for the jobs.
After it fails miserably, if you blame the model, then you should become a software engineering.
If you blame the algorithm, then you should become a data scientist.
Sounds like data science for you. But I'm curious: What is your academic background exactly? Where are you learning machine learning without programming?
> Where are you learning machine learning without programming? A math degree. You're confusing data engineering and data science. There are plenty of people who work on theory and do little to no programming.
Well, given that I made a statement about machine learning and not data science, the point still stands.
Machine learning is a CS field. It emerged out of CS. Any claims to the contrary are hokey revisionism. As to what "data science" entails, that's become a super loaded buzzword, so I'm not even sure where to begin. And "data engineering," please don't even. Just fancy terms for statistics and discrete math.