winter17 - resume

[jxnl]

Resumes should tell one story.

Jason Liu

There are a few things I'd like to highlight when thinking about the data science resume.

• What stage in your career are you?
• Define Data Science.
• Who is the audience?

Career Stage

• Are you transitioning careers?

  • software (backend, frontend)
  • academic (masters, PhD)

• Do you have any experience?

  • first years?

• Are you cold applying or going through referrals?

Define Data Science

• What do you want to do:
  • visualization
  • modeling
  • analysis
  • consulting
  • implementation

Who is your audience

• smaller companies

  • don't have HR
  • data scientist/manager may be looking at your resume
  • you may have to 'wear many hats'

• larger companies

  • recruiters may pre-screen
  • only looking for keywords
  • just demonstrate skill in required task

Filler

• early stage, we get it. you have to fill stuff in
• once you get experience remove asap,
  • jason only includes topics that can be conversation starters,
  • why ask me about my sql side project rather than my SQL at work? drop the side project.
  • this is only true because most my jobs are not cold applications, my resume is for my interviewer not my recruiter.
• early in my career i didn't care about my education and put a lot of effort in highlighting my MOOCs, readings, and coursework.

[jxnl]

I might also (near the end) discuss stuff to do during the interview. (what questions to ask, is this the right job for me)

[sarthfrey]

Maybe add something about providing stats in points for exp and projects, I find interviewers/recruiters love points like "Did X and generated Y% user growth over a period from A to B" or something like that. Shows you had real impact and legitimizes yourself as a candidate.

[sarthfrey]

Also I completely agree with the conversation starter line, the best projects seem to be the ones that are interesting and interviewers want to talk to you about them

[sarthfrey]

Also at some point it's worthwhile mentioning that often on WaterlooWorks (jobmine etc) there are job positions that aren't data science but actually involve data science work, so good to look out for those too

[sarthfrey]

A few other points:

• Mention what technologies you used (Ex: Python, Spark, R, etc)
• Try to incorporate impact in points for experience/employment
• In projects don't use the name of the project if it's unknown to the reader (Ex: if you built a spelling corrector called spelly, no one knows what spelly is just write "spelling corrector" or something
• Add links to github and email and phone number etc
• Make your name somewhat big and readable
• If you worked at a less known company with a cool role, emphasize the role in your formatting, and vice versa if you worked at a prestigious company
• When applying on WaterlooWorks, put your education at the bottom unless you have some really relevant coursework or moocs or smth because they already know you go to UW and know what program you're in