Enhance Recruiters Productivity and Search Quality Using Machine Learning
October 27, 2020
American multinational human resource consulting firm
Problem Statement
- Legacy systems need to be replaced to aid faster delivery cycles, meeting higher %age of SLAs, and greater insights into candidate profiles.
- To develop sophisticated IT and Data Science infrastructure to enable end-to-end talent acquisition for clients right from receiving job orders to finding candidates
Solution Approach
- Candidate Halo ingests data from Oracle data base
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Data is stored in XML object format
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Identify clusters within 800 job titles
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Unsupervised machine learning models
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Integrated Machine Learning Model
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NLP framework to identify key concepts from conceptual job description
Business Impact
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End-to-End SLA from search to match is 6-10 seconds for complex search
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Improvement in Employability rate from 1.5% to 3.0% post production
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Expanded candidate identification, richer job/skills mapping
Critical Success Factors
- Feature Extraction
- Candidate Halo scores candidates In near-real-time
