The Challenge
A recruitment firm approached us mid-process. They were already several weeks into a senior hiring engagement and had accumulated a significant amount of panellist scoring data — but no consistent framework for interpreting it.
Different panels had scored differently. Some panellists were generous; others were strict. Without a way to normalise the data, the final shortlist risked reflecting panellist personality as much as candidate merit.
Our Solution
We introduced a lightweight data review process that could be applied retrospectively to the existing scores without disrupting the client relationship or the panel’s confidence in their own judgement.
Working with the scores already collected, we identified statistical patterns across panels, flagged anomalies, and built a normalised view of candidate performance. We then walked the hiring team through the data — not to override their judgement, but to give them a clearer picture to make decisions from.
The approach was designed to be collaborative, not clinical. People adopt data-driven processes when they feel ownership of the outcome.
The Result
The hiring team left the engagement with a shortlist they could defend analytically, a process they understood, and — critically — a new appreciation for what structured data review could do for future hiring cycles. One team member described it as “seeing the interviews for the first time.”
