Optimizing Talent Acquisition Using Machine Learning Algorithms in Maruti Suzuki India Limited

In today’s competitive business environment, acquiring the right talent efficiently is critical for organizational success. Traditional recruitment methods often rely on subjective judgment, leading to inefficiencies and potential biases. This study aims to optimize the talent acquisition process at Maruti Suzuki India Limited by leveraging machine learning algorithms to enhance decision- making, reduce hiring time, and improve candidate-job fit. The research explores various machine learning techniques, such as logistic regression, decision trees, random forests, and support vector machines, to predict candidate success and retention based on historical recruitment data. Data was collected from Maruti Suzuki’s internal HR systems and analyse for patterns related to qualifications, experience, skill sets, and interview performance. Feature engineering and model evaluation were conducted to identify the most accurate and interpretable model. The findings demonstrate that machine learning can significantly streamline the recruitment process, offering actionable insights into candidate selection, improving objectivity, and reducing human bias. The study concludes with recommendations for implementing a data-driven recruitment model that aligns with Maruti Suzuki’s strategic HR goals. This research contributes to the growing field of HR analytics and provides a practical framework for deploying AI-driven talent acquisition solutions in the Indian automotive industry.