👉 Hiring math involves quantifying the relationship between candidate attributes and job performance to optimize recruitment decisions. It typically uses statistical models, such as regression analysis, to predict outcomes like job success, retention, or performance based on factors like education, experience, skills, and cultural fit. By analyzing historical data, hiring managers identify which attributes correlate most strongly with positive results, then use this insight to set benchmarks (e.g., minimum required skills) and create targeted hiring formulas. This approach helps reduce bias, improve candidate matching, and ensure resources are allocated efficiently, though it requires careful validation to avoid overfitting or overlooking nuanced human qualities.
