  This paper presents a comprehensive study on resume classification to reduce
the time and labor needed to screen an overwhelming number of applications
significantly, while improving the selection of suitable candidates. A total of
6,492 resumes are extracted from 24,933 job applications for 252 positions
designated into four levels of experience for Clinical Research Coordinators
(CRC). Each resume is manually annotated to its most appropriate CRC position
by experts through several rounds of triple annotation to establish guidelines.
As a result, a high Kappa score of 61% is achieved for inter-annotator
agreement. Given this dataset, novel transformer-based classification models
are developed for two tasks: the first task takes a resume and classifies it to
a CRC level (T1), and the second task takes both a resume and a job description
to apply and predicts if the application is suited to the job T2. Our best
models using section encoding and multi-head attention decoding give results of
73.3% to T1 and 79.2% to T2. Our analysis shows that the prediction errors are
mostly made among adjacent CRC levels, which are hard for even experts to
distinguish, implying the practical value of our models in real HR platforms.
