HireFit
A logistic-regression hiring model on four years of talent data — on-target hires up to 83%, mis-hire rate down 41%.
Predicting On-Target Hires from Talent Data
Cutting the mis-hire rate by 41% and lifting on-target hires to 83% didn't take more interviews; it took evidence. HireFit is a logistic-regression model trained on four years of talent data, scoring every candidate against the profiles that actually succeeded instead of relying on gut feel.
Challenge
Mis-hires were expensive and frequent, and candidate matching relied on gut feel — with no systematic way to learn from past hiring outcomes.
Approach
Built a logistic-regression hiring model trained on four years of talent data, scoring candidates against the profiles that had actually succeeded in the organisation.
Outcome
On-target hires rose to 83% — a 15 percentage-point improvement — while the mis-hire rate fell by 41%.
Stack & Methods
- Logistic regression
- Four years of talent data
- Candidate scoring against successful hire profiles
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