A lot of you must be wondering how does @graph actually audit a Data & AI resume.
So thought of sharing what's under the hood.
Every bullet point gets checked against 7 things:
What: the work itself. What was built, changed, fixed or delivered.
How: the tools, languages, platforms or methods used.
Scale: how big the work was. Data volume, record counts, number of pipelines or users, budget. "Large-scale" with no figure is vague. And a percentage never counts as scale.
How much: a measured result. A percentage, time saved, cost, a before and after. "Improved" or "faster" with no number is vague.
Why: the problem or purpose behind the work. What was wrong before.
Your role: was it actually your work? Built, led, owned pass. Helped, supported, "worked on" are vague.
Outcome: who it helped. Users, the team, the business, a system. If the effect is only implied, it's vague.
I kinda see where you’re going with this. The whole idea of having a single place to keep all project related context and then using it how / when I want seems interesting. Good luck. @graph
@samrin_pathan Thanks a lot!!
Congrats guys @vikramkparekh @lucky7x_ . Sooo needed. Being pressure tested is probably a great way to improve our chances to crack interviews. @graph
@ahona_nandi Thanks a lot.
@vikramkparekh @ahona_nandi Thanks, Ahona! We really hope graph helps people walk into interviews with a much better understanding of how to talk about their own work. 😊
Great idea! Excited to see this develop further.
@martin_janse_van_rensburg Thanks a lot for your encouragement. Means a lot the team.
@martin_janse_van_rensburg Thanks, Martin! It's been quite a journey getting this out there. Excited for what's ahead and looking forward to learning from everyone using it.