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Job Candidate Screening Problem: Data-driven approach to evaluate profile quality on Crafthunt

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Summary:This project addresses the job candidate screening process of the German company Crafthunt, a job platform specialized in the construction industry. With an average of 500 candidate profiles created monthly, manually screening them has become a tedious process that consumes significant resources of the company's small Talent Management Team. Among researchers, this organizational challenge is referred to as the job candidate screening problem. To solve this problem, multiple clustering algorithms and analytical models were employed to create a candidate segmentation based on key profile quality indicators. While the DBSCAN algorithm did not deliver satisfactory results, the application of the k-means algorithm created 4 distinct clusters with a strong silhouette score of 0.69, separating candidates based on their language skills, profile completion, geographical location, and verification status. Through the application of the RFM model, three quality tiers of candidates were built based on their platform engagement. The project successfully met all set objectives and provided Crafthunt with two segmentation approaches along with recommended actions for implementation.
Main Authors:Kohl, Amelie
Subject:Recruitment Artificial Intelligence Clustering Construction Job Platforms SDG 8 - Decent work and economic growth
Year:2025
Country:Portugal
Document type:master thesis
Access type:embargoed access
Associated institution:Universidade Nova de Lisboa
Language:English
Origin:Repositório Institucional da UNL

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