Graduating now with a Physics PhD from Harvard doing mostly experimental work, recently squeezed in some device simulations and modeling. The only CS class in my entire life I have taken is called Data Science which I found myself pretty comfortable in. Spent the past 6 years on coding side projects: webapps, IoT devices and backend, blockchain apps and an internship at a tiny quant fund. So, 0.5 YoE I guess.
I am sick of academic lab research and want to move into a coding/fintech job. Started leetcoding on Easys after an online DS&A crash course. Did some ML online courses and tried applying them to my PhD research. I'm familia with Python, Node, SQL, Mongodb, numpy-pandas-mpl, docker, React.
1. Should I do DS or SWE? I prefer WLB over TC. TC should ideally be over 100k.
2. How/What are the chances of leveraging my PhD so I did not waste the prime years of my life on this paper? What level will I be entering at?
Looking in the Boston area, maybe NY.
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Data science isn't enough to get into a FANG. You need to learn data structures and algorithms. Languages don't matter at all for entry level interviews. Pick one of (python, Java, c++), find a coding interview book and supplement that with an algorithms text (for reference), a book in your language (or audit a course), and plenty of leetcode medium.
It will be easier to get into an ML or applied ML role, partly because you'll understand advanced math concepts better than your average code monkey and partly because those orgs still place value on original PhD research. But that doesn't matter if you can't pass the interview loop.
Build your Harvard physics network as much as you can while you're still there. That's the main thing your PhD work will have going for you, and its value may not become clear for a few years but there is value in it for networking, job referrals, etc a few years down the road. More of them than you think will end up in industry in three years. Make friends with CS people if you can, that will build your network further.
levels.fyi