A compensation model for US-based tech companies
"All models are wrong, but some are useful." — George Box
It’s an open secret that benchmark databases are hit-or-miss: great coverage for some roles (mid-levels in engineering, product, etc.) and some locations (SF, NY). But how much equity should you grant a Staff Engineer in Memphis at a Series B company? "Error, low sample size."
Fortunately, it turns out this is a very solvable problem with data science. We analyzed thousands of data points across public and proprietary data to design a predictive model that produces 4+ million combinations: 200+ titles, 700+ locations, and 30+ company stages (seed-stage to FAANG). Check out our white paper & FAQs below for more detail.
We hope you find our model useful.