People Analytics Mathematical Model for Talent Decisions

This white paper presents a mathematical model developed by ELEKS aimed at enhancing talent decision-making within large corporations. It addresses common challenges faced by HR managers, including performance measurement, skills gap identification, risk assessment of employee turnover, and effective filling of open positions. The document outlines the creation of a simulation context to design and test the people analytics algorithm, which utilizes a comprehensive dataset reflecting organizational structures, employee profiles, and business rules. By leveraging this model, companies can make informed decisions rather than relying solely on intuition. The paper details a case study involving the internal recruitment process for a Beauty Marketing manager position, demonstrating how the model analyzes candidate profiles to identify the best fit based on various criteria, including education, experience, and skills. The findings suggest that data-driven approaches can significantly reduce hiring risks and improve overall talent management strategies.