Employee Churn Prediction
Talenthacker's, Catenon's leading digital talent selection division, has always been distinguished by its innovative approach, making data and technology the pillars of its strategy. However, as industry leaders, we are faced with the challenge of accurately discerning who, among the professionals identified in their database, are truly willing to change jobs. This task is complicated by high competition and the difficulty of evaluating candidates' true intention, resulting in selection efficiency that we know we can optimize.
At WhiteBox, we have designed a solution that has transformed the way Talenthacker's recruiters work. Our system, powered by an extensive database of candidates and their CVs, applies advanced Machine Learning and natural language processing techniques (with tools such as scikit-learn, LightGBM, and Hugging Face Transformers) to predict with high accuracy those professionals who are truly ready for a change. This approach allows us to consider key factors such as the length of the current position, the type of employment, the company, and relevant skills, among others, to identify the most promising candidates.
The implementation of our predictive model in the Talenthacker selection process has marked a turning point in the recruitment of digital talent in the company. Now, recruiters can focus their efforts on candidates genuinely interested in new professional opportunities, significantly optimizing performance and results. This strategy has not only increased recruitment efficiency, it has also positioned Talenthacker at the forefront of their sector, benefiting both the speed and the quality, as well as the profitability, of their processes.