Great example of how data fidelity is important. A key aspect of any application, especially in this age of AI, is how reliable and actionable the data underneath is. Compa provides compensation teams with exactly that. A platform backed by a vast realtime data set to better understand the market and act rapidly to changes. And there is so much more coming this year to further that mission! Charlie Franklin Joseph Delmonico Malandruccolo Taylor Cone Bobby Dysart Storm Ventures Link in the comments below
What can comp teams do with skills data? The most basic use case is to benchmark with better precision. I illustrated this in my newsletter post this morning with a simple analysis of two similar open jobs at Uber: - Same company, level, location, and posted salary range, but clearly different titles - so either they share a job code powering the range or they have a disclosure strategy to limit what they share - Compose "custom jobs" in Compa using the skills in the job description for each of them, e.g. Pytorch, Python, Node.js, Go - The offers-based market data for reveals each job commands materially different pay in the market - $163k-$193k for the SWE Ads role, $192k-$230k for the MLE role - The posted range midpoint aligns to the 44th percentile for the MLE role and above the 95th percentile for the SWE role, yet both appear to have the same range That's a 16% difference for base salary. The new hire equity medians vary by over 50%. Without skills, these jobs appear to be the same. With skills, they are in completely different talent markets. Once you start to analyze the market with skills data, it's hard to unsee it... Link to my newsletter post and analysis below. #compensation #totalrewards