  In this paper, we describe our method for ranking the skills required for a
given job title. Our analysis shows that important/relevant skills appear more
frequently in similar job titles. We train a Language-agnostic BERT Sentence
Encoder (LaBSE) model to predict the importance of the skills using weak
supervision. We show the model can learn the importance of skills and perform
well in other languages. Furthermore, we show how the Inverse Document
Frequency factor of skill boosts the specialised skills.
