HR Tech
Resume Parsing & Ranking
Turns a pile of resumes into a ranked, explainable shortlist — combining visual NLP for layout-aware extraction with a scoring model and a dashboard for comparing candidates side by side.
The problem
The most time-consuming, error-prone, and monotonous step in hiring is a human reading resumes to work out who is worth a conversation. For an HR outsourcing company doing this at volume, it was a bottleneck that scaled linearly with headcount and got less accurate as it went.
What I built
A resume ranking system in three parts:
Extraction. Information retrieval from the resumes themselves, including visual NLP — resumes are laid-out documents, not plain text, and column order, tables, and headings carry meaning that a naive text dump destroys.
Scoring. A mathematical model that scores and ranks candidates against the role, producing an ordering rather than a binary filter.
Reporting. A dashboard for the humans who make the actual decision: comparing multiple ranked candidates against each other, drilling into the detail behind a score, and cross-validating claims on a resume against multiple external public sources.
That last part matters — the system is built to be checked. A ranking nobody can interrogate does not get trusted, and does not get used.
The outcome
- Sharply reduced the time taken to hire.
- Freed HR capacity for the parts of recruitment that need a person.
- Fewer errors in screening, with cross-validation catching what a fast manual scan misses.