This project implements an AI-driven text segmentation and classification system designed to organize large volumes of job descriptions into meaningful occupational categories using semantic similarity and taxonomy alignment.
The system processes unstructured job text, identifies semantic patterns, and maps each group to standardized O*NET / SOC job families, enabling large-scale labor-market analysis, search, and reporting.
The pipeline is built to be scalable, domain-agnostic, and production-ready, allowing the same architecture to be adapted to other text domains such as resumes, ecommerce content, or customer messages.
- Segments thousands of job descriptions into coherent occupational clusters
- Aligns each cluster with the most relevant SOC / O*NET job codes
- Produces similarity-scored mappings for transparency and validation
- Outputs structured datasets for dashboards, reporting, and downstream analytics
- Text preprocessing and normalization
- Semantic embedding of job descriptions
- Cluster-based text segmentation
- Similarity matching against O*NET job definitions
- Taxonomy-based classification and ranking
- Automated structured output (CSV, database, dashboards)
(Detailed implementation is intentionally abstracted to preserve reusability and protect IP.)
Traditional job classification relies on manual tagging or rigid keyword rules, which do not scale and fail to capture semantic nuance.
This system uses embedding-based similarity and clustering to model how jobs are actually described, enabling:
- Faster classification at scale
- Higher consistency across datasets
- More reliable labor-market analytics
The system is backed by a PostgreSQL-based data store optimized for large-volume text and vector data, allowing it to process tens of thousands of job records efficiently without performance bottlenecks.
The same architecture can be reused for:
- Resume databases
- Ecommerce product catalogs
- Marketing messages
- Customer support tickets
- Any large unstructured text dataset
- Python-based NLP pipelines
- Semantic embeddings & similarity search
- Clustering & taxonomy alignment
- Relational and vector-enabled data storage
- Structured data export and dashboards
Our embedding-based clustering grouped together not only traditional accounting firms, but also accounting SaaS, CFO advisory firms, and accounting staffing agencies, reflecting the full labor-market ecosystem around the Accountants & Auditors occupation.