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.
