Hiring Match Score
Match your profile against job descriptions to assess fit
Introduction
Hiring Match Score analyzes job descriptions against your profile to tell you how well you fit a specific role. Instead of guessing if you're qualified, you get a data-driven match percentage.
Why It Exists
Reading job descriptions is subjective. You might think you're a great fit, but miss that the role requires 5 years of Kubernetes experience you don't have. Hiring Match removes the guesswork.
How It Works
1. Job Description Parsing
Paste any job description into the text area. The system extracts:- Must-have skills: Languages, frameworks, tools explicitly required
- Nice-to-have skills: Preferred but not mandatory qualifications
- Role level: Junior, Mid, Senior, Staff (detected from keywords)
- Domain: Frontend, Backend, Full Stack, DevOps, etc.
2. Evidence Gathering
Your profile data is analyzed to find evidence of required skills:- GitHub languages: JavaScript, Python, Go, etc.
- Framework inference: React/Next.js inferred from JavaScript usage
- LeetCode topics: Algorithm skills from problem-solving data
- Codeforces rating: Competitive programming proficiency
3. Score Calculation
Each requirement is weighted by priority:- Critical skills: 3x weight
- High priority skills: 2x weight
- Nice-to-have skills: 1x weight
Your match score is: (Skills Matched / Total Required) x 100
Framework Inference
The system is smart about frameworks. If a JD requires "React" and you have JavaScript in your GitHub languages, you get credit because React is built on JavaScript. Supported inferences:
- React, Next.js, Vue, Angular → JavaScript/TypeScript
- Django, Flask, FastAPI → Python
- Spring Boot → Java
- Rails → Ruby
- Express, Node.js → JavaScript
What You Can See
Match Score
A percentage showing overall fit (0-100%).Verified Matches
Skills you have that match the JD requirements, with evidence source.Missing Skills
Gaps between your profile and the job requirements.Risk Assessment
Potential red flags like low consistency or missing critical skills.Profile Snapshot
Quick stats: GitHub repos, LeetCode solved, overall score.Example JDs
Quick-fill buttons for "Senior Backend" and "Junior Frontend" examples.Verdict System
Based on your score, you receive a verdict:
- 90%+: Strong Match - High confidence to apply
- 75-89%: Good Match - Apply with confidence
- 50-74%: Moderate Match - Consider for phone screen
- Below 50%: Weak Match - May not be the right fit
Who Should Use This
- Job seekers evaluating role fit before applying
- Developers targeting specific roles for skill development
- Career changers assessing transition requirements
- Anyone wanting objective feedback on job eligibility
Real-World Value
- Stop wasting time applying to unqualified roles
- Identify exactly which skills to develop for dream jobs
- Build confidence before interviews with data
- Compare fit across multiple job listings
Common Scenarios
Pre-Application Check
Paste a dream company's JD to see if you match before investing time in the application.Skill Gap Discovery
See that you're missing Kubernetes for cloud roles—now you know what to learn.Interview Prep
Know your weak areas so you can prepare talking points for gaps.FAQs
Does it work with any JD format? Yes, the parser handles most job description formats. Longer, more detailed JDs produce better results.
Why did I get 0% even with relevant skills? Check if the JD requires frameworks (React, Django) that weren't directly detected. The inference system handles common cases, but some niche frameworks may not be recognized.
Can I save my match results? Currently, results are session-based. Saving and history features are planned.
How accurate is the role level detection? It looks for keywords like "senior", "junior", "staff", "lead". If these aren't in the JD, it defaults to "Mid" level.