Enhance Recruiters Productivity and Search Quality Using Machine Learning

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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
  • Data is stored in XML object format

  • Identify clusters within  800 job titles

  • Unsupervised machine learning models

  • Integrated Machine Learning Model

  • NLP framework to identify key concepts from conceptual job description

Business Impact

  • End-to-End SLA from  search to match is 6-10  seconds for complex search

  • Improvement in Employability rate from 1.5% to 3.0% post production

  • Expanded candidate identification, richer job/skills mapping

Critical Success Factors

  • Feature Extraction
  • Candidate Halo scores candidates In near-real-time

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