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How can I use Data Structure to match a resume to JD?

RChilli Support
  • Updated

To effectively use a data structure to match a resume to a job description (JD) using RChilli, the process involves three main components: parsing, taxonomy-based enrichment, and search & match APIs. Here’s a structured explanation of how this works:

Step-by-Step Guide: Using Data Structures to Match Resume to JD

1. Parse Resume and JD into Structured Data

Use RChilli’s Resume Parser and Job Parser APIs to convert unstructured documents (PDF, DOCX, etc.) into structured JSON data formats.

  • Resume Parser extracts 200+ fields including name, education, skills, experience, and more.

  • Job Parser extracts job title, required skills, qualifications, and responsibilities from the JD.

This structured data enables consistent, machine-readable formats for both resumes and JDs.

2. Leverage RChilli's Taxonomy 3.0 for Standardization

RChilli’s Taxonomy 3.0 includes over 3 million skills and 2.4 million job profiles.

  • It maps extracted skills and job titles to standardized terms, including aliases, abbreviations, and related terms.

  • This ensures semantic consistency—for example, “Software Developer” and “Programmer” can be linked as equivalents.

Using this, both the resume and JD can be enriched with:

  • Alternate skill/job titles

  • Domain-specific tags

  • Synonyms and comparable job/skill terms

This is essential for effective matching.

3. Use Search & Match API for Intelligent Matching

RChilli’s Search & Match API uses indexed data and an AI-powered scoring mechanism to match resumes with JDs.

Supported Matching Modes:

  • Job to Resume: Best candidates for a job.

  • Resume to Job: Best job matches for a candidate.

  • Job to Job: Similar jobs for recommendation engines.

  • Resume to Resume: Candidate clustering based on profile similarity.

How it Works:

  • The API uses skills, domain, location, job title, experience, education, and company data to generate match scores.

  • The scoring mechanism is based on configurable weights and taxonomies to provide relevance-based ranking.

Business Value of Data Structures in Matching

Component Value
Structured JSON Enables consistent data comparison and mapping
Taxonomy Mapping Enhances matching accuracy via skill/job profile normalization
Search & Match Scoring Engine Provides ranked results, eliminating guesswork in candidate/job fit

 

API Integration Flow

  1. Parse the Resume and JD using API endpoints

    • parseResumeBinary or parseResume for resume

    • JD parser endpoint for job descriptions

  2. (Optional) Call Taxonomy API to standardize skills and job roles.

  3. Index parsed data using ParseAndIndex API for Search & Match functionality.

  4. Call Search & Match API

    • Use endpoint like /searchResumeByJD or /matchResumeToJob.

  5. Use match scores and metadata to determine best fit candidates or jobs.

Helpful Resources

Need a Hands-On Demo?

You can join the RChilli Incubator Program to test these APIs in a sandbox environment.

If you need help implementing or testing this, feel free to reach out at support@rchilli.com.

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