How Orin™ Matches Candidates to Your Jobs
When you post a job on NexArc, Orin™ runs a multi-dimensional match against every candidate whose profile signals relevance. Here’s what happens behind the scenes:
The Scoring Pipeline
- Skills Match (40% of score) — Orin™ compares required skills in your JD against the candidate’s listed skills, project tech stacks, and GitHub activity. It understands skill adjacency (“React” and “Next.js” are related).
- Experience Fit (25%) — Years of experience, past roles, project complexity, and domain relevance.
- Culture Signals (15%) — Remote preference alignment, communication style (from feed activity), and work-style indicators.
- Profile Quality (10%) — Profile completeness, portfolio quality, recommendation signals.
- Activity Recency (10%) — Active users who engage on the platform are scored higher than dormant profiles.
The Nightly Cycle
Orin™ runs scoring jobs each night. By 8 AM, your applicant dashboard shows updated rankings with transparent score breakdowns. You see exactly why each candidate scored what they did.
What You See
For each applicant, you get:
- Overall match score (0–100)
- Breakdown by dimension (skills, experience, culture, quality, activity)
- Highlighted strengths and gaps
- Orin™ recommendation: “Strong match”, “Good match”, “Review manually”, or “Low fit”
How to Get Better Matches
- Be specific in your JD — “2+ years Python + FastAPI” gives better matches than “backend experience”
- Separate must-haves from nice-to-haves — Orin™ weights must-haves much higher
- Include salary range — Eliminates mismatched expectations early
- Set the right job type — Full-time, part-time, internship, freelance each attract different profiles
The Workflow
- Post a job with clear requirements
- Orin™ scores and ranks applicants overnight
- Review your dashboard each morning
- Shortlist high-scoring candidates
- Message candidates directly through NexArc
- Schedule interviews and make offers
Privacy & Fairness
Orin™ never uses gender, age, caste, religion, or any protected attribute in scoring. Matching is based purely on skills, experience, and professional signals. All scores are auditable and transparent.