Data Scientists & ML Engineers
Job Market Training Data for AI & ML Teams
39,000+ skill labels, 3,300+ certifications, and 100% seniority coverage across 1B+ records
Pre-enriched, deduplicated job market training data. Skip 6 months of pipeline building.
AI and ML teams building labor market models need high-quality, labeled training data. Canaria provides pre-enriched, deduplicated records with consistent schemas across years, eliminating months of data pipeline construction. Every record comes with SOC codes, seniority labels, skills lists, and salary predictions already attached.
Common Challenges
✕Building a scraping and NLP pipeline for job data takes 6-12 months before a single model can be trained
✕Schema inconsistencies across sources mean years of data cannot be concatenated without extensive normalization
✕Duplicate records inflate training sets and teach models to replicate noise rather than signal
✕No ground truth labels for skills, salary, SOC codes, or seniority means training on proxy labels with unknown accuracy
How Canaria Helps
- ✓Pre-labeled with SOC codes, skills, salary predictions, seniority, and work mode across every record
- ✓Consistent schema across the entire historical archive from 2022 to present, ready to concatenate
- ✓Semantic deduplication using vector similarity and graph-based transitive matching removes training data noise
- ✓1B+ records across 200K+ sources for statistically robust model training across diverse employer types
Example Use Cases
- 1Fine-tune a job title normalization model using 1B+ records with ground truth SOC codes and normalized titles
- 2Train a salary prediction model using stated salary fields as labels across 50M+ annotated observations
- 3Benchmark a skills extraction model against 39K+ labeled skills across 1B+ annotated job descriptions
Relevant Data Fields
normalizedTitlesocCodetechnicalSkillssoftSkillsannualSalaryAverageUSDseniorityremoteStatusdescriptionThese are a subset of the 100+ fields available in every Canaria record.
Related datasets and insights
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