How is proficiency level calculated and captured for skills?

RChilli calculates and captures proficiency levels for skills using its Resume Parser and Taxonomy frameworks. Here’s how this is handled in detail:

🔍 How Proficiency Level is Calculated for Skills

1. Skill Segregation with Source Context

RChilli's parser extracts skills from resumes and segregates them using the SkillSegregation object. This includes:

• Skill Type: Operational, Soft, or Behavioral

• Source Section: Indicates whether the skill is mentioned in the Experience, Projects, or Education section

• Taxonomy Mapping: Associates skills with standardized values for better matching and classification

This segregation enables the system to understand where the skill was acquired or mentioned, which provides context that is essential in estimating proficiency.

2. Experience-Based Proficiency (When Available)

If the resume explicitly mentions a skill along with an experience duration (e.g., "Java – 5 years"), and it appears in structured sections like Work Experience or Projects, RChilli captures this:

• The Skill field can include:

  • SkillName

  • ExperienceInMonths (e.g., 60 months for 5 years)

  • LastUsed (e.g., 2023)

📌 Important Note: This data is only extracted if explicitly mentioned. If no duration is provided, proficiency is not assumed or fabricated. This ensures data reliability.

🧠 Use of Taxonomy for Enhanced Skill Understanding

RChilli’s Taxonomy 3.0 plays a significant role in enriching skills with related metadata:

• Standardized Proficiency Indicators: Using synonyms, alternate skill names, and related job profiles.

• Ontological Grouping: Skills are categorized into domains/subdomains, which helps in defining expected proficiency levels based on job context.

You can also call the Taxonomy API to fetch enriched skill data with attributes like skill type, domain, and related competencies.

🛠 Skill Scoring & Ranking in Search & Match

In Search & Match API, the proficiency level affects how candidates are ranked:

• Scoring Logic includes:

  • Skill frequency

  • Experience duration (ExperienceInMonths)

  • Recency (LastUsed)

• Skills can also be sorted by:

  • ExperienceInMonths

  • LastUsed

  • Ontology relevance

This information is used to compute matching scores in job-candidate comparisons, helping recruiters prioritize candidates with stronger demonstrated skills.

✅ Summary

If you have any questions, feel free to drop an email at support@rchilli.com