TheGrkIntrprtr

Hi HN,

Nearly finished my CS postbacc degree with a perfect GPA while working. All hell broke loose as soon as I started studying, with never ending family issues throughout, which really stressed me out.

I made a couple of contributions to the Firefox browser in C++. The 2nd one 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. This was very rewarding personally and a great learning experience, but so far has not resulted in any increased attention from job applications (have sent out many recently with the contributions listed on my resume) – zero interviews, all rejections so far. I’m all over the place, can’t seem to pick a direction & it seems I am crap at planning out my career. Picking a direction and focusing on it seems necessary in this competitive environment.

which option should I choose to increase earnings in the future (in Canada, can’t move to USA)?

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) try to pivot into the data engineering team in my area and go all in on DE

4) focus on leetcode interview prep

5) OSS contributions in languages such as Java/C# (I thought of trying to contribute to Apache Cassandra)

6) focus on business ideas instead of employment, given high taxes & low Canadian salaries: learn webdev and start a SaaS

7) learn gamedev and develop an indie game (probably ridiculous for me to pursue, web/mobile dev seems more practical..though I don’t have experience in either)

8) other?

I’m thinking the most pragmatic choice would be to focus on ML (option 2), maybe go for a masters at some point, create a LLP/corp for ML consulting when I’m confident and invest in S&P 500. This is a long road though, and I feel like I’m just wasting my time whenever I crack open an ML textbook to start studying. In fact, I get this uncomfortable feeling with any other direction I consider at the moment.. since the only work exp. I have is in analytics & resume screeners/recruiters don’t seem to care about anything other than work experience. ———-

Background:

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 not enough for buying property), 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 should note that I’m not willing to take a salary cut… part of my motivation is feeling pressured to increase my income as life in Canada is becoming less affordable every day (housing crisis etc.). I’m over 30 and still renting.

Should I just refresh ML/stats/math knowledge (which is very rusty after years in a slow moving environment) and contribute 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 another 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, and it feels like I’m just spinning my wheels. Any suggestions are appreciated!

Hi HN,

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!

How difficult would it be to make decent contributions to firefox in C++, given I'm new to the language? I work as a data analyst programming in R, SAS, and SQL, and have taken a single programming course based on HTDP. My thinking was that learning how to navigate and contribute to a large complex codebase would be a good way to self-teach in an attempt to transition to a software development role. While this would mostly be for professional reasons, I do use firefox and it'd be satisfying to contribute.

I received some advice recently, saying that I'd be better off following my alternative self-taught plan of building a foundation from learning Python, and that I'd be taking on a bit too much trying to contribute to such a large scale project without a solid foundation. I was going to follow what Trevor Blackwell suggests in his FAQ (http://tlb.org/#faq), learning Python and C, and reading through some Python libraries and the interpreter written in C. Python is directly applicable to my job and could perhaps help me land a data engineering role.

Which plan would you suggest? Any idea how long it would take to get to the point where I'd be able to make decent contributions to such a large project? Thanks!

I have an unrelated degree, BSc Econ, and I love the programming (R, SQL) side of my job as a data analyst. For personal reasons, I'd like to remain working in the public sector, so I can't really transition to a dev role internally without the credential. Data engineer roles don't seem to exist here either. Have to make a decision of whether to resume a second bachelor's in CS in September, or give it up entirely. Can't do part time studies, got an A- in Gregor Kiczales' CPSC 110 course at UBC based on HtDP, which was GREAT, but too stressful while working full time. I'm in my late 20s.

I was spending some time learning Unix on OpenBSD, as well as Vim and Python, and enjoying it. But I can't help feeling I'm wasting my time without the piece of paper, if my goal is to be able to spend my days building software - even if it were maintaining some old crusty old system in Java, I think I'd enjoy it more. And the time I'm spending on that means I'm not spending it on keeping up my skills for my data analyst role, like brushing up on ML (though I'm not really interested in this..) etc.

The problem is, the opportunity cost of going back is hard to stomach. Giving up a decent salary, paying tuition, moving away (3 hrs away or so) from family members after being away from them for so long, it all seems so costly that I'll regret it. However, if I don't go through with it, I think I'd regret that too - that I didn't take the chance to be doing something else for the next 30+ yrs that I'd enjoy more.

So, I could use some advice on a way forward. Is being an excellent dev even feasible if it isn't your day job? And if anyone has gone through something similar, I'd love to hear how it went for you - did you regret going back to school, or not, and why?

For anyone who reads this, thank you for your time.