In this session, Daniel will present ongoing research that the ILO is conducting with partners in academia and in the corporate world, revealing a jobs network that was generated with the help of data science techniques. The network offers some insights about what kind of job transitions people make in their careers, and which transitions are blocked.
This session will discuss how individuals, organisations and governments can better detect opportunities for job transitions using new methods from data science. We will explore what are the implications for skills policies, and the question – can we imagine a world in which we work without jobs?
Why this is important right now:
Many jobs are currently at risk of being either automated or transformed
Artificial intelligence (AI) and COVID-19 are two main drivers that are churning global labour markets
Career transitions for many may be necessary. Yet, we still know little about what kind of job-to-job transitions people successfully make or could be making in the future.
This session will explore:
- Future career paths
- Job-to-job transitions
- Network analysis.
Key takeaways – attend and learn:
- What kind of job-to-job transitions do currently exist: which ones happen more frequently than others and which transitions do not happen
- What we can say about workforce skills that are transferable to other jobs and that facilitate transitions
- How natural language processing (NLP) and network analysis can improve our understanding of career paths.