#2023 new grad looking for feedback! :)
1 messages · Page 1 of 1 (latest)
"Results – what happened as a result of your action and what you learned/gained from the experience (!!!!!!!!). It can be helpful to include some metrics if applicable, but don't take too much sole credit for things you did as part of a team. Remember, highlighting your ability to work in a team is a benefit not a drawback."
"SELECTED PROJECT EXPERIENCE" i think this is a weird title but maybe just me
minor but i'd separate JS from HTML/CSS
"GitHub/Lab" -> "GitHub/GitLab"
or just remove the gitlab part
" VSCode, IntelliJ IDEA" remove
and you also list bitbucket separately?
you don't really need semicolons at the end of each bullet imo
"resulting in 15% fewer bugs" by what analysis?
@languid girder thanks for the tips thus far
"Programmed Python test scripts in Linux" in Linux?
if you list the stat you should be able to back it up with more than that imo
at least explain more about the reduction because it's a bit odd in isolation
"Presented to key shareholders with completed updates and demonstrated functionality of feature updates leading to approval"
underutilised and poorly explained imo
this can be a v good thing
just needs to be executed better
"• Offer accepted, position canceled due to COVID-19" remove this
"Led the development of instructional videos for X, a job placement application for servicemen, by coordinating with team
members and by communicating in person and remotely to establish key goals, adhere to strict budgets, and meet hard deadlines;"
this sentence does not work. it's too long and flows badly
"supporting backend development;" vague
you are not doing this
Make sure that everything you put on your CV has a purpose. Every single word should be there for a reason. Really sit there and think about why you are writing certain things. Are you just finishing the sentence on auto-pilot, or are you writing with purpose? Remove the fluff/waffle.
"while overseeing the collection of 1000s of necessary assets as well as the editorial process." vague
"Object detection deep CNN in Python"
this entire bullet is too dense and doesn't really explain why any of this is being done
"for domain adaptation" ?
"Trained the models on 1000s of simulated images, leading to predictions on real extraterrestrial surface images of varying altitudes."
this feels so close to a very cool sentence
training models on simulated images that are close to the real thing in hopes of accurately predicting on real images
ok, if it's standard language then it's fine
i'm gonna leave the feedback there for now. i suggest re-reading my bullet points as well
gl
thank you for your time. i really appreciate every bit