Contextual bandits for staffing in consulting companies: An exploration of personalized decision making

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Journal Title

Journal ISSN

Volume Title

Perustieteiden korkeakoulu | Master's thesis

Authors

Date

2023-10-09

Department

Major/Subject

Computer Science

Mcode

SCI3042

Degree programme

Master’s Programme in Computer, Communication and Information Sciences

Language

en

Pages

65

Series

Abstract

Staffing is critical for consulting organizations as they seek to identify and hire the most qualified and motivated individuals for a given job. Traditional staffing methods rely on manual evaluation, assessment, and selection. However, these methods may not always be efficient or effective in identifying the most suitable candidates, especially in dynamic and complex environments with many employees. In this thesis, we explore personalized decision making by conducting a proof of concept study to propose using contextual bandits, a class of machine learning algorithms, as a framework for staffing. Contextual bandits are designed to make decisions or suggestions based on contextual information, which can be useful in identifying the most suitable candidate for a given job. First, we collect data on the job requirements and candidates’ competencies, interests, and skills, we clean this data and format it. Second, develop contextual bandit exploration models using Vowpal Wabbit library. Third, we perform hyperparameter tuning of the parameters in control of exploration-exploitation trade- off. Forth, we select the best-performing exploration algorithm. After that we discuss the findings. Finally, we create a dashboard for the staffing and resource management teams to use the trained algorithm to make optimal staffing decisions, considering job requirements and candidate attributes. In the end, we discuss the areas for further development. Overall, our research provides evidence that contextual bandits can be a powerful tool for staffing, providing an efficient and effective way to identify the most qualified candidates for a given job. Our framework can help organizations make better staffing decisions and improve their overall performance and employee satisfaction.

Description

Supervisor

Jung, Alex

Thesis advisor

Jung, Alex

Keywords

contextual bandits, reinforcement learning, vowpal wabbit, streamlit, staffing, consulting

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Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202310156397

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[dipl] Perustieteiden korkeakoulu / SCI