  Machine learning plays an ever-bigger part in online recruitment, powering
intelligent matchmaking and job recommendations across many of the world's
largest job platforms. However, the main text is rarely enough to fully
understand a job posting: more often than not, much of the required information
is condensed into the job title. Several organised efforts have been made to
map job titles onto a hand-made knowledge base as to provide this information,
but these only cover around 60\% of online vacancies. We introduce a novel,
purely data-driven approach towards the detection of new job titles. Our method
is conceptually simple, extremely efficient and competitive with traditional
NER-based approaches. Although the standalone application of our method does
not outperform a finetuned BERT model, it can be applied as a preprocessing
step as well, substantially boosting accuracy across several architectures.
