Despite advancements made in quantifying job and skill data, career progression remains a largely randomly-driven path. In this presentation, we will introduce a Linked-Job Data framework used to connect seemingly distinct areas of talent management: mobility, recruitment, learning programs, along with universal market trends such as job automation or remote work.
NLP algorithms allow it to decipher the activities behind a job title as well as to identify the skills imparted by a given training program. As a result, one can quantify the skill gap between current and future jobs and prescribe training programs to bridge this gap. We will use several case studies to illustrate the best practices and the limitations of applying machine learning to career progression. The examples cover topics such as the importance of soft skills in recruitment, the projected impact of job automation on careers, and the science of remote work.