  The recently launched LinkedIn Salary product has been designed with the goal
of providing compensation insights to the world's professionals and thereby
helping them optimize their earning potential. We describe the overall design
and architecture of the statistical modeling system underlying this product. We
focus on the unique data mining challenges while designing and implementing the
system, and describe the modeling components such as Bayesian hierarchical
smoothing that help to compute and present robust compensation insights to
users. We report on extensive evaluation with nearly one year of de-identified
compensation data collected from over one million LinkedIn users, thereby
demonstrating the efficacy of the statistical models. We also highlight the
lessons learned through the deployment of our system at LinkedIn.
