DeepAI AI Chat
Log In Sign Up

Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary

by   Krishnaram Kenthapadi, et al.

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.


PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn

Preserving privacy of users is a key requirement of web-scale analytics ...

GPTIPS 2: an open-source software platform for symbolic data mining

GPTIPS is a free, open source MATLAB based software platform for symboli...

Insights from the Wikipedia Contest (IEEE Contest for Data Mining 2011)

The Wikimedia Foundation has recently observed that newly joining editor...

Visualization in the preprocessing phase: an interview study with enterprise professionals

The current information age has increasingly required organizations to b...

Design and Analysis of the NIPS 2016 Review Process

Neural Information Processing Systems (NIPS) is a top-tier annual confer...

Explainable Artificial Intelligence for Improved Modeling of Processes

In modern business processes, the amount of data collected has increased...