Selecting Excellence: A Data-Driven Process to Hire Top Performance Engineers With Greater Speed and Certainty

Traditional hiring methods are unstructured: data limited, emotion-based decisions that cause lower profits, project delays, avoidable costs, and legal risks. This paper defines a practical data-driven process that solves these challenges. Using this framework, hiring teams can easily organize position-specific selection criteria that align critical interviewing activities and fuels data rich hiring decision-making. This process has been deployed at small and large organizations, resulting in a data-driven process with more effective decision-making faster hiring, increased quality-of-hire, reduced costs, and lower risks.