Analyzing talent flow behavior is important for the understanding of job preference and career progression of working individuals. When analyzed at the workforce population level, talent flow analytics helps to gain insights of talent flow and organization competition. Traditionally, surveys are conducted on job seekers and employers to study job behavior. While surveys are good at getting direct user input to specially designed questions, they are often not scalable and timely enough to cope with fast-changing job landscape. In this paper, we present a data science approach to analyze job hops performed by
about a population of working professionals. We develop several metrics to measure how much work experience is needed to take up a job and how recent/established the job is, and then examine how these metrics correlate with the propensity of hopping. We also study how
talent flow behavior is related to job promotion/demotion. Finally,we perform network analyses at the job and organization levels in order to derive insights on talent flow as well as job and organizational competitiveness.
Philips is the principal engineer in Living Analytics Research Centre of Singapore Management University. His research interests include social media mining, job analytics, and machine learning.
Dr Lim Ee Peng is a professor at the Singapore Management University (SMU). His research interests include social media analytics, information integration, and information retrieval. At SMU, he is also the Director of the Living Analytics Research Centre, an NRF supported research centre focusing on data analytics research for smart nation application domains.
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