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Data Interview Question

Resume Processing and Search Pipeline

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Requirements Clarification & Assessment

  1. Purpose of the Tool

    • The primary goal is to convert resumes into searchable text data to facilitate internal analytics and enhance the recruitment process.
    • The tool should support both machine learning (ML) models for natural language processing (NLP) and keyword monitoring for company analysts.
  2. Key Outcomes

    • Data Mart Creation: A repository for ML models to access processed text data.
    • Data Product Development: A tool for analysts to monitor specific keywords within resumes.
    • Search API Implementation: An interface for recruiters to perform keyword searches on processed resumes.
  3. Assumptions

    • Image-to-text (I2T) conversion models are pre-existing and reliable.
    • The system does not require real-time processing but should ensure swift data availability.
    • Privacy and security measures are managed by a separate team.
    • Scalability requirement: handling up to 1000 images per 24 hours.
    • Cloud-based infrastructure is preferred for high availability.
  4. Clarifying Questions

    • What specific insights are desired from the analytics?
    • Are there particular regions or formats of resumes that need special handling?
    • What are the key performance indicators (KPIs) to measure the tool's success?