Yeah that's fair -- maybe just focusing on becoming an expert in data and ML engineering in the long-run is a more prudent career choice than totally dropping those for another corner of engineering. There's some wisdom here.
Taking that much time off of any job is pretty hard to do!
The real goal of a bootcamp though (besides networking) is adding a project to your portfolio. You can then show off this project to hiring managers to demonstrate your competence.
You don't need to do a full-time bootcamp to make this happen! I just checked and Flatiron school and Hack Reactor both have online part-time full-stack bootcamps. Also, while not as big of an investment, online platforms like Codecademy have career paths with a capstone project you can complete and add to your portfolio.
So, overall there are a variety of pathways to do this. Totally fair to point out though a bootcamp of some kind is a nice option in the off-chance it's a possibility.
I didn't end up doing a bootcamp in the end. I sort of wish I did: friends of mine who did ended up with some strong connections as a result. Then again, someone else I know had an extremely negative experience with Hack Reactor and lost thousands of dollars in it, so maybe it's for the best that I didn't go.
You're right, Google has been very supportive -- very thankful for that! That said, if you work at a technology company, you CAN build a portfolio and self-advocate for projects with engineering teams your data science team already works with.
I think it's worth seeing how far you can get with this approach before interviewing for engineering roles at other companies.
One notable caveat with my approach: pursuing a portfolio approach took me a whole year to switch from DS to engineering. As you point out, grinding leetcode may only require half that time.
Yes -- one thing I struggle with is choosing whether to work on data engineering projects (which I know I'm good at and can provide immediate value) or challenging myself with more "traditional" full-stack engineering work where I can learn a lot.
I have always been an engineer, but when I started as an intern in 2015, most of my work centered around helping analyses: cleaning up CSVs, data mining, minor processing and transformations, etc. None of it was connected to production but these were ad-hoc requests. I think this start was invaluable to my general philosophy today (devops or die), and believe there are a lot of missed opportunities identifying the right analysts that should take the next step into complexity and help craft better devops processes for analysts. They're developers too.
One thing I don't think gets talked about enough: How much time analysts spend doing analysis vs. time spent dealing with data quality/architecture/process. So much analyst time is lost in the latter, which I think contributes greatly to burnout. But the growing intersection with engineering has and will continue to address this, albeit slowly.
I run a small data consulting company and whenever I find an open minded data scientist or analyst I tell them to consider data and software engineering or business analytics (learning about real business problems). DA, BA and SE roles are harder to find, manage more complexity, but most importantly are closer to having an impact as they are the ones close to prod. It’s a hard pitch, especially for juniors, AI and DS are very hyped but it’s hard to guess how much they will grow (or not). My current hunch is that AI and DS will be productized in libraries and services, so the majority of companies will not need people fully dedicated to build models.
My educational background is much closer to data science (Math with CompSci Minor B.Sc., Data Science M.Sc.) but I've never worked in a pure data science role.
Training and tweaking models looks like the easy part of developing data driven products. Hiring compenent enough people also seems easier than for software engineers.
Many ML libraries produce good enough results without having to design elobare models myself. I see data science more as yet another tool in the belt of a a software engineer - like server admin, CI/CD, IaC, databases,...
Hi! I'm Zach, the author -- I put together this doc since I couldn't find many resources I could recommend on metric design and evaluation, which pops up in data scientist/product analyst interviews and is a common task for data science teams.
Looking to hearing your feedback and if you've seen other good resources on this topic!
Hi! I'm Zach, the author -- I put together this doc since I couldn't find many resources I could recommend on metric design and evaluation, which pops up in data scientist/product analyst interviews and is a common task for data science teams.
Looking to hearing your feedback and if you've seen other good resources on this topic!
