  The impact of person-job fit on job satisfaction and performance is widely
acknowledged, which highlights the importance of providing workers with next
steps at the right time in their career. This task of predicting the next step
in a career is known as career path prediction, and has diverse applications
such as turnover prevention and internal job mobility. Existing methods to
career path prediction rely on large amounts of private career history data to
model the interactions between job titles and companies. We propose leveraging
the unexplored textual descriptions that are part of work experience sections
in resumes. We introduce a structured dataset of 2,164 anonymized career
histories, annotated with ESCO occupation labels. Based on this dataset, we
present a novel representation learning approach, CareerBERT, specifically
designed for work history data. We develop a skill-based model and a text-based
model for career path prediction, which achieve 35.24% and 39.61% recall@10
respectively on our dataset. Finally, we show that both approaches are
complementary as a hybrid approach achieves the strongest result with 43.01%
recall@10.
