Exploring the Role of Data Science in University Staff Recruitment and Selection in Nasarawa State University, Keffi, Nasarawa State
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Background of the Study
Effective recruitment and selection of university staff are critical to maintaining high academic standards and operational efficiency. At Nasarawa State University, Keffi, Niger State, traditional recruitment processes, which rely heavily on manual review of applications and subjective evaluations, have often resulted in inconsistent hiring outcomes. Recently, the emergence of data science in human resource management has provided new tools for optimizing recruitment practices. Data science techniques, such as predictive analytics and machine learning algorithms, can process vast amounts of data from applicant profiles, academic achievements, and professional experiences to identify candidates who best match the institution’s requirements (Ibrahim, 2023). These systems enable more objective decision-making by standardizing evaluation criteria and reducing human bias. Furthermore, data-driven recruitment models can identify trends and patterns in successful hires, thereby refining future selection processes. By integrating multiple data sources—including performance metrics, interview feedback, and demographic information—data science enhances the ability to forecast candidate success and cultural fit within the university (Chinwe, 2024). However, while the promise of a data-driven approach is considerable, several challenges must be addressed, such as data integration, ensuring data quality, and managing privacy concerns related to sensitive applicant information (Adebayo, 2023). This study aims to explore how data science can transform staff recruitment and selection at Nasarawa State University by comparing traditional methods with data-driven approaches, evaluating their respective impacts on recruitment efficiency and quality, and providing recommendations for best practices in using analytics for human resource management (Balogun, 2025).
Statement of the Problem
The recruitment and selection processes at Nasarawa State University are currently characterized by a lack of standardization and an overreliance on subjective assessments, which often lead to inconsistencies in hiring outcomes (Ibrahim, 2023). Traditional recruitment methods are labor-intensive and time-consuming, and they do not effectively leverage the wealth of data available about applicants. As a result, the university struggles with identifying the best candidates and achieving a good fit between staff skills and institutional needs. Although data science offers a promising alternative by providing objective, data-driven insights, its adoption is hampered by challenges such as fragmented data sources, inadequate data quality, and concerns over data privacy and ethical use of applicant information (Chinwe, 2024). Additionally, there is resistance from some HR personnel who are accustomed to conventional methods and may be skeptical about the reliability of predictive models. This gap between traditional and data-driven recruitment practices has resulted in suboptimal staffing decisions that affect the overall performance of the university. Therefore, it is essential to evaluate the role of data science in enhancing the recruitment process and to develop a framework that addresses the technical, ethical, and operational challenges associated with its implementation (Adebayo, 2023; Balogun, 2025).
Objectives of the Study:
• To develop a data science framework for improving staff recruitment and selection.
• To compare the outcomes of traditional and data-driven recruitment methods.
• To propose strategies to enhance data integration, quality, and privacy in recruitment processes.
Research Questions:
• How does data science improve the accuracy of staff recruitment decisions?
• What are the main challenges in implementing data-driven recruitment methods?
• What strategies can enhance data quality and privacy in the recruitment process?
Significance of the Study
This study is significant as it investigates the application of data science in improving staff recruitment and selection at Nasarawa State University, offering insights that can lead to more objective, efficient, and transparent hiring practices. The findings will inform HR policies and ultimately contribute to enhanced institutional performance (Ibrahim, 2023).
Scope and Limitations of the Study:
This study is limited to the evaluation of recruitment and selection processes at Nasarawa State University, Keffi, Nasarawa State.
Definitions of Terms:
• Data Science: The interdisciplinary field of extracting insights from large datasets using computational methods (Chinwe, 2024).
• Recruitment and Selection: The process of attracting and choosing candidates for employment (Ibrahim, 2023).
• Predictive Analytics: Techniques used to forecast future outcomes based on historical data (Balogun, 2025).
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