Detailed Data Dictionary: https://docs.google.com/spreadsheets/d/1JKUYZYPNZfcg5Ol9LTk8fwe5hwiu7c5DSn-3Wia7mo8/edit?gid=1071313126gid=1071313126

Developed by a seasoned team of ML experts from Google, Meta, and Amazon and alumni of Stanford, Caltech, and Columbia, our AI-powered pipeline provides invaluable insights for HR tech, lead generation, market intelligence, and corporate development. With cutting-edge AI and LLMs, we transform raw job postings into actionable data, analyzing job titles, skills, predicted salaries, locations, and more.

Each posting undergoes multi-layered processing, with GPU-driven models delivering daily, weekly, and monthly data for a balanced real-time and historical view. Our processing pipeline integrates advanced AI models:

• Deduplication Model: Filters out exact and near-duplicates, ensuring unique, high-quality job data.
• Title Taxonomy Model: Categorizes over 20 million titles into 50,000 standardized groups, simplifying analysis.
• Skill & Qualification Models: Extracts hard skills, soft skills, certifications, and degrees, mapped to standard education levels and tailored by job context.
• Job Category Model: Predicts work types (remote, onsite, hybrid), seniority, and employment types (full-time, contract).
• Location Prediction Model: Parses or estimates job locations—ZIP code, city, state—using averaged lat/long values from search-based estimates for accuracy.
• Salary Estimation Model: Predicts minimum, average, and maximum salaries, using parsed and AI-predicted values for a robust salary range.
• Government Classification Models: Assigns SOC codes and titles to postings for structured role insights and regulatory compliance.
• Human Feedback Model: An in-house team of annotators reviews job descriptions and AI outputs, refining model accuracy.

Delivered through S3, FTP, and Google Drive, Canaria’s dataset provides flexibility in integration, with APIs available on request. Combining real-time AI with human validation, Canaria’s data delivers business-ready insights to meet evolving HR and market demands.

Core Industry Applications - HR & Workforce Analytics: Access insights into salary trends, workforce demographics, and skill demands to drive strategic HR decisions. - Lead Generation: Identify target leads and hiring needs through granular job postings data. - Investment & Market Intelligence: Gain insights into competitor hiring strategies and industry shifts. - Education & Skill Development: Support curriculum development and training programs based on skill trends and emerging job requirements. - Corporate Development: Align growth strategies with real-time job market data. - Talent Sourcing: Streamline talent sourcing by identifying active job markets and regions with the highest demand for specific skills. - Job Market Forecasting: Analyze hiring trends and job postings data to forecast demand for specific roles and skills. - Economic Research: Provide labor market insights for economic studies, helping to assess job growth and employment shifts by industry.

Graph and download economic data for Wage and salary accruals per full-time equivalent employee: Domestic private industries: Bituminous and other soft coal mining (H4409C0A052NBEA) from 1929 to 1947 about accruals, coal, full-time, mining, salaries, private industries, domestic, wages, private, employment, industry, GDP, and USA.

Advanced Processing, Superior Insights

Utilizing state-of-the-art AI and large language models (LLMs) validated by human experts, we are dedicated to delivering high-quality, actionable salary and payroll data through innovative technology.

Apart from the models included in our standard data offerings, we have developed additional models to provide tailored results to your needs, such as a sentiment analysis model that analyzes salary data to gauge sentiment, helping businesses understand public perception and employee feedback, anomaly detection models, and LLM-based summarization models that condense large chunks of salary data for you.

Our Models:

• Deduplication Model: Our model first removes exact duplicate records, then uses advanced AI to identify and eliminate near-duplicate job postings across different URLs, achieving approximately a 60% deduplication rate, ensuring unique salary and payroll data. • Title Taxonomy Model: With over 20 million unique job titles in our 500M+ job postings database, salary data analysis can be challenging. Our AI models categorize each job posting into one of 50,000 standardized job titles from our internal normalized title taxonomy, simplifying salary data analysis. • Skill Taxonomy Model: Our in-house AI model identifies key entities in job postings, including hard skills, soft skills, certifications, and qualifications. Unlike keyword-based approaches, our model not only finds relevant keywords but also excludes irrelevant ones, ensuring precise salary and payroll data (e.g., "Hepatitis B" is a skill for nursing jobs but not for accounting jobs). • Job Category Model: Our AI models analyze job descriptions, entities, predicted salary, location, industry, and job title to determine the seniority level of a job, standardizing levels across different companies. Another model identifies if a job is remote, onsite, or hybrid, accounting for discrepancies between job classifications and descriptions (e.g., a job classified as onsite but open to remote), enhancing salary and payroll data accuracy. • Salary Estimation Model: Using company salary history, industry ranges, location, seniority, and public government data, our models predict the salary range for job postings, providing comprehensive salary data. • Government Classification Models: We developed models to classify job postings into Standard Occupation Codes (SOC) by the BLS and to categorize companies into industries based on their job posting information, enriching salary and payroll data.

Data Sourcing

• Multiple Data Sources: Data is aggregated from top US job boards, including Indeed (approximately 80%), LinkedIn, other leading job posting websites, and company career pages, ensuring high-quality salary and payroll data. • Advanced Web Scraping: Advanced web scraping techniques are utilized to collect job postings hourly. However, enhancing the data with AI-LLM models takes time, so salary data is delivered daily to ensure high-quality results. • Human-Labeled Annotations: AI & LLM models are trained and verified with human-labeled annotations to ensure the highest accuracy in salary and payroll data classification and attribute extraction. • Data Deduplication: Rigorous data deduplication processes are implemented to eliminate redundant job postings, ensuring the uniqueness and quality of the salary and payroll data. • Continuous Data Validation: Salary and payroll data undergo continuous validation processes, including cross-referencing with multiple sources, to maintain accuracy and reliability. • Quality Assurance: A dedicated team is responsible for ongoing quality assurance, ensuring the salary and payroll data remains comprehensive, accurate, and actionable for clients.

Core Use-Cases and Industry Applications of Salary and Payroll Data

HR Tech: • HR Analytics: Gain insights into industry demands, salary benchmarks, and job market trends to support strategic HR decisions. • HR Strategy: Develop and implement effective HR strategies based on comprehensive salary data. • HR Intelligence: Analyze job market salary data to optimize HR practices and improve talent acquisition.

Lead Generation: • Lead Generation: Utilize salary data to identify potential leads and understand the hiring needs of prospective clients. • Account-Based Marketing (ABM): Tailor marketing efforts to specific accounts based on salary data trends. • Lead Data Enrichment: Enhance lead data with detailed salary information.

Business Intelligence (BI): • Employment Analytics: Analyze job market trends and employment data to support business decisions. • Competitive Intelligence: Compare salary data trends across different companies and industries to gain competitive insights. • Competitor Insights: Understand competitors' hiring activities and strategies.

Market Research: • Market Research: Conduct research on labor market dynamics, employment trends, and skill demand using salary d...

Initial Jobless Claims in the United States increased to 223 thousand in the week ending March 15 of 2025 from 221 thousand in the previous week. This dataset provides the latest reported value for - United States Initial Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The average per-employee spending on learning and development (L&D) worldwide increased steadily between 2008 and 2019, however fell slightly in 2020. This figure increased again in 2021, reaching almost 1,300 U.S. dollars per worker. There was then a 4.7 percent expenditure decrease in 2022.

Workplace learning and development
Learning and development (L&D), a crucial area of human resource management, is a process aimed at improving an employee’s skills, knowledge, and competency, so to achieve better performances in the workplace. Despite a decrease in 2020 because of the COVID-19 pandemic, the global market size of the workplace training industry increased considerably since 2009. North America alone accounted for almost half of the global market. The growing relevance of workplace training can also be inferred by the increase in the workplace learning hours for employees worldwide.

L&D promotes employee engagement
Employee engagement supports growth through a wide range of benefits, including higher productivity and profitability, and more satisfied customers. Overall, learning & development might help supporting employee engagement. Investing in learning shows employees that they are valued, which generally increases their motivation in the workplace. Employees’ support for L&D is suggested by the considerable share of young workers perceiving it as a useful method to find opportunities within the organization. Moreover, for an effective L&D implementation it is useful to consult employees about their expectations: according to a 2019 survey, half of employees worldwide felt the urge to improve their influencing and negotiating skills. Furthermore, the most popular learning method among employees worldwide was learning in a classroom with a group of colleagues.