  Analyzing job hopping behavior is important for understanding job preference
and career progression of working individuals. When analyzed at the workforce
population level, job hop analysis helps to gain insights of talent flow among
different jobs and organizations. Traditionally, surveys are conducted on job
seekers and employers to study job hop behavior. Beyond surveys, job hop
behavior can also be studied in a highly scalable and timely manner using a
data driven approach in response to fast-changing job landscape. Fortunately,
the advent of online professional networks (OPNs) has made it possible to
perform a large-scale analysis of talent flow. In this paper, we present a new
data analytics framework to analyze the talent flow patterns of close to 1
million working professionals from three different countries/regions using
their publicly-accessible profiles in an established OPN. As OPN data are
originally generated for professional networking applications, our proposed
framework re-purposes the same data for a different analytics task. Prior to
performing job hop analysis, we devise a job title normalization procedure to
mitigate the amount of noise in the OPN data. We then devise several metrics to
measure the amount of work experience required to take up a job, to determine
that existence duration of the job (also known as the job age), and the
correlation between the above metric and propensity of hopping. We also study
how job hop behavior is related to job promotion/demotion. Lastly, we perform
connectivity analysis at job and organization levels to derive insights on
talent flow as well as job and organizational competitiveness.
