This system functionality develops a machine learning system to intelligently match workers with job opportunities based on historical activity, behavioral signals, and job characteristics. Designed to improve employment outcomes and operational efficiency, the model balances automation with human oversight, enabling teams to scale matching decisions while maintaining control and explainability.
The Worker Matching Model leverages historical job data, behavioral patterns (e.g., churn, engagement, reliability), and job metadata to:
Recommend top candidates for a given job posting
Score the compatibility of job-worker pairs
Surface reasons for match strength to support recruiter decisions
It is a decision-support tool tailored for workforce platforms and HR systems. The system is designed for B2B use, particularly for internal HR departments, staffing platforms, or employment marketplaces.
Semi-autonomous decision support: Suggests candidates but keeps recruiters in the loop.
Behavioral and demographic inputs: Uses job history, attendance, and engagement data; may include basic demographic data for fairness checks.
Flexible retrieval architecture: Embedding-based similarity search using FAISS or similar.
Embedding training via contrastive learning over worker-job interactions
Retrieval and scoring with vector databases (e.g. FAISS)
Explainability through SHAP-based attribution and metadata surface
Human-in-the-loop interface via internal APIs or platform dashboards
This system is trained using:
First-party behavioral and performance data
Public job descriptions and taxonomies
Optional demographic or sensitive fields for bias auditing (never used for decisions directly)
No personal data from minors or high-risk categories is used in production environments without additional safeguards.
git clone https://github.com/your-org/worker-matching-model.git
cd worker-matching-model
pip install -r requirements.txt
🧪 Usage python Copy Edit from model import WorkerMatcher matcher = WorkerMatcher() scores = matcher.score(worker_profile, job_posting)
Core model training
Embedding export
Explainability support
Real-time API (WIP)
Fairness dashboard (in progress)
The model is part of workforce management tools and may influence economic opportunities by helping workers connect with better-fitting jobs. Its deployment is intended to support and not replace human recruiters.
Key considerations: Domain: Employment / HR
Impact scale: Moderate – affects platform users and HR teams
Potential impacts: Fairness in job access, economic inclusion
Transparency: Designed to provide recruiter-facing rationales
Robustness: Stress-tested across edge cases; security measures in place for inference APIs
Vulnerable groups: Attention to potential disparate impact across protected groups
📄 License MIT License. This project is meant for ethical use in improving job access and platform fairness.