Labour Market Pricing Model

Overview

I analysed how job listing signals such as role level, skills, work arrangement, location, demand, and talent supply can be used to estimate fair salary bands. The project reframes salary benchmarking into a data-driven labour market pricing framework, showing how salary drivers can guide skill funding, training allocation, and workforce planning decisions.

Problem: Predicts fair salary bands from job listings using interpretable machine learning

Outcome: Designed a factor-based salary benchmark framework that turns job listing data into actionable salary bands, helping decision-makers identify high-value skills, adjust training budgets, and respond faster to labour market shifts.