User Story Name: Core Ranking Algorithm
User Story: As a Recruiter, I want the system to calculate a comprehensive match score for each candidate based on resume skills, industry experience, education, and management level, so I can quickly assess their objective qualifications against the job requirements.
Definition of Done:
The core match score aggregates resume skills, industry experience, education, and previous job title and management level. 
A System Admin user can individually weight and configure each scoring criterion. 
The algorithm must successfully process and assign a score between 0–100% to 99% of uploaded resumes.

User Story Name: Cultural Fit Scoring
User Story: As a Recruiter, I want the AI to calculate Cultural Fit as part of the ranking, so I can prioritize compatible candidates.
Definition of Done:
A machine learning model for Cultural Fit is trained, validated, and deployed. 
The Cultural Fit score is displayed separately from the core match score. 
The system provides a human-readable, auditable rationale for the cultural fit score (e.g., via a tooltip).

User Story Name: Automated Candidate Discard
User Story: As the Recruiter, I want the system to automatically decline candidates with scores below 70%, so we interview only high-potential candidates.
Definition of Done: 
Candidates with a core match score of < 70% are moved to a Discarded archive queue (not permanently deleted). 
The 70% threshold is configurable via a system-level setting. 
The system sends an automated notification to the assigned Recruiter when it auto-discards a candidate.

User Story Name: Bias & Compliance Audit Trail
User Story: As a Recruiter, I want AI to flag potential screening bias with HR Policies, so that we remain compliant.
Definition of Done: 
The system must generate a "Bias Audit Trail" for every rejected candidate. 
The ranking algorithm must exclude all prohibited demographic data fields from its calculation inputs. 
The system is integrated with the legacy Payroll system’s API for real-time salary verification to prevent non-compliant salary offers. 
The entire AI-generated candidate ranking process must be auditable for bias.

User Story Name: Stakeholder Dashboard
User Story: As a Hiring Manager, I want a simple dashboard of ranked candidates, so that I can quickly decide whom to interview first.
Definition of Done: 
The dashboard displays Core Match Score, Cultural Fit Score, and Candidate Name/Summary in a single view. 
The dashboard is filterable by both Core Match Score and Cultural Fit Score (low-to-high, high-to-low). 
The dashboard includes a single-click action to move a candidate to the Interview Scheduled status.