I’d like to ask for some career advice.
TL;DR:
which option should I choose? 1) continue contributing to Firefox and focus on SWE applications; 2) refresh ML/stats knowledge and contribute to numpy or other data related OSS and apply for data scientist/ML positions; 3) focus on leetcode interview prep; 4) other? ———- Background:
Nearly finished my CS postbacc degree with a perfect GPA while working, and I’m wondering which direction to pursue - whether to double down on my data analysis background/work experience or pursue a role more focused on SWE. I’m a Canadian resident.
I made a couple of contributions to the Firefox browser in C++, which was very rewarding personally and a great learning experience, but so far has not resulted in any increased attention/interview rate from job applications.
I work in IT in the public sector (pension, decent benefits etc), but the role is focused on supporting data analysts/scientists in the business area. I also have a bachelors in Econ, and prior to this job, I was a data analyst for years (and got the attention of a manager in IT, which is how I secured this job). The pay is decent, but everything moves slowly and I’m not learning much. Too much paperwork, and hardly any software dev. I’d like to experience another environment, for a year at least, and I’d be happy with either a SWE or data scientist/ML eng position. I’d like to make this transition in around 3 months if possible. I should note that I’m not willing to take a salary cut… life in Canada is too damned expensive!
I’m wondering if I should continue contributing to Firefox, fixing more difficult bugs. It would definitely improve my software dev skills. It was my first experience navigating a large codebase, and the second contribution, which didn’t seem very complex at first, very quickly had me delving into C++ template metaprogramming with meta functions to complete it. The mentor at Mozilla gave me some great tips as well. Obviously though, I’m not programming in C++ in my day job. An alternative route would be to refresh my ML/stats/math knowledge (which is very rusty after years in a slow moving environment) and contribute instead to data related OSS like numpy or PyTorch, read textbooks like “mathematics for machine learning”, and focus on getting a data scientist/ML eng position. I question the ROI for this though since I don’t have a MS/PhD. I am also not ready for a leetcode style interview, so maybe a third option is to just focus on interview prep. I’m trying to do 1 leetcode per day while taking a CS course, working, contributing to OSS, and reading textbooks… I’m very possibly spread too thin. This makes my rejected applications all the more depressing. Any suggestions are appreciated!
