Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary
This work addresses salary transparency for professionals, but it is incremental as it applies existing statistical methods to a new dataset.
The paper tackles the challenge of providing robust compensation insights to professionals by developing a statistical modeling system for LinkedIn Salary, using Bayesian hierarchical smoothing and evaluating it on one year of data from over one million users.
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.