Applications of machine learning have the potential to significantly impact upon the role of Human Resource Management by automating both novel and existing objectives that are difficult or costly to achieve currently. One such example is the ability to predict employee attrition and identify its underlying causes. This has the potential to not only improve employee retention but also productivity, well-being and robustness. However, this potential also harbours additional challenges and demands. The successful application of a machine learning approach is reliant on appropriate quantity and quality of data. This could lead to increased monitoring and tracking of quantified employee performance and engagement. This may involve data gathering perceived as intrusive by employees such as location, activity and behaviour monitoring. Conversely, without appropriate data machine learning may be more prone to increased error which could have significant potential impact on individuals in the form of improper predictions.
This research will explore the above challenges through applied research. It aims to examine the practicalities of applying machine learning to turnover prediction, the identification of key indicators in attrition and how this might inform data-driven retention strategies.
To Design, build and evaluate an employee turnover prediction tool
To examine the performance, capabilities, limitations and challenges of such a system applied in a real-world context.
Evaluate predictive performance of a range of supervised machine learning approaches to a real-world data set. Use statistical methods to identify and describe correlated variables from the real-world data. Use qualitative/quantitative methods to measure impact of related factors and how they influence decision making from either employee and employer perspective.
Applicants should have or be able to evidence:
• Education to Masters Degree level in a relevant area.
• A First or Upper Second (2.1) Honours Degree
A sound understanding of, and interest in several of the following areas:
• Machine Learning / Artificial Intelligence
• Human Computer Interaction
• Qualitative / Quantitative Research Methods
• Ability to contribute to research study design
• Computer literacy
• Proficiency in oral and written English
• Ability to organise and meet deadlines
• Good interpersonal skills and ability to work independently and contribute to a team
• Commitment and an enthusiastic approach to completing a higher research degree
• Existing connections/links with appropriate communities/groups
• Human Resource Management
For further information please contact:
Dr Chris Bowers, Head of Dept. Computing, Worcester Business School ([email protected]
Dr Lynn Nichol, Head of Dept. Mgmt. and Finance, Worcester Business School ([email protected]
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