Canaria | LinkedIn Data Job Postings | US | 5M+ Monthly LinkedIn Job Postings & 6 Month Historical | AI-LLM Enhanced Deduplicated Unique LinkedIn Data

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 LinkedIn job postings 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 LinkedIn job postings 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 LinkedIn job postings data for you. Our Models: • Deduplication Model: Our model first removes exact duplicate records, then uses advanced AI to identify and eliminate near-duplicate LinkedIn job postings across different URLs, achieving approximately a 60% deduplication rate, ensuring unique LinkedIn job postings data. • Title Taxonomy Model: With over 20 million unique job titles in our 500M+ job postings database, LinkedIn job postings 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 LinkedIn job postings data analysis. • Skill Taxonomy Model: Our in-house AI model identifies key entities in LinkedIn 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 LinkedIn job postings data (e.g., "Hepatitis B" is a skill for nursing jobs but not for accounting jobs). • Job Category Model: Our AI models analyze LinkedIn job posting 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 LinkedIn job postings data accuracy. • Salary Estimation Model: Using company salary history, industry ranges, location, seniority, and public government data, our models predict the salary range for LinkedIn job postings, providing comprehensive LinkedIn job postings data. • Government Classification Models: We developed models to classify LinkedIn job postings into Standard Occupation Codes (SOC) by the BLS and to categorize companies into industries based on their job posting information, enriching LinkedIn job postings data. Data Sourcing Multiple Data Sources: Data is aggregated from top US job boards, including LinkedIn, other leading job posting websites, and company career pages, ensuring high-quality LinkedIn job postings data. Advanced Web Scraping: Advanced web scraping techniques are utilized to collect LinkedIn job postings data hourly. However, enhancing the data with AI-LLM models takes time, so LinkedIn job postings 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 LinkedIn job postings data classification and attribute extraction. Data Deduplication: Rigorous data deduplication processes are implemented to eliminate redundant LinkedIn job postings, ensuring the uniqueness and quality of the LinkedIn job postings data. Continuous Data Validation: LinkedIn job postings data undergoes 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 LinkedIn job postings data remains comprehensive, accurate, and actionable for clients. Core Use-Cases and Industry Applications of LinkedIn Job Postings 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 LinkedIn job postings data. • HR Intelligence: Analyze LinkedIn job market data to optimize HR practices and improve talent acquisition. Lead Generation: • Lead Generation: Utilize LinkedIn job postings 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 LinkedIn job postings trends. • Lead Data Enrichment: Enhance lead data with detailed LinkedIn job market information. Business Intelligence (BI): • Employment Analytics: Analyze job market trends and employment data to support business decisions. • Competitive Intelligence: Compare LinkedIn job postings 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 LinkedIn job postings data. • Job Market Pricing: Analyze LinkedIn job postings and salary data to establish market pricing for various roles. • Job Pricing: Determine competitive salary ranges for job postings based on comprehensive LinkedIn job postings data analysis. Machine Learning (ML) & Natural Language Processing (NLP): • Machine Learning (ML): Develop ML models to predict LinkedIn job market trends and enhance job matching algorithms. • Natural Language Processing (NLP): Utilize NLP techniques to extract and analyze LinkedIn job postings data for improved insights. Corporate Development: • Corporate Development: Inform strategic initiatives and business growth plans with detailed LinkedIn job market data. • Hiring: Optimize hiring strategies and identify talent acquisition opportunities using LinkedIn job postings data. Job Boards Listings: • LinkedIn Data: Leverage data from LinkedIn to gain insights into job market trends and hiring practices. • LinkedIn Job Posting Data: Utilize LinkedIn job postings data to understand industry-specific hiring trends. Integration with Broader Offering Complementary Data Integration: The LinkedIn Job Postings Data and Title & Skill Taxonomy Data seamlessly integrate with each other and other data products offered by Canaria Inc. This integration provides a comprehensive view of the job market, skill trends, and industry movements. Enhanced Data Insights: By combining LinkedIn job postings data with title & skill taxonomy data, users gain a multi-dimensional perspective on job market dynamics, workforce trends, and required skills. This holistic approach enables more informed decision-making across various business functions. Scalable Solutions: These data products are part of a scalable suite of solutions catering to businesses of all sizes. Whether for small businesses or large enterprises, clients can leverage these datasets alongside other offerings to support growth and strategic initiatives. Customizable Data Solutions: Canaria Inc. provides tailored data solutions that can be customized to meet specific business needs. LinkedIn job postings and title & skill taxonomy data can be enriched with additional data layers, such as demographic information or economic indicators, to deliver targeted insights. Innovative Technology: Utilizing advanced AI & LLM models verified by human experts, these data products exemplify Canaria Inc.'s commitment to leveraging cutting-edge technology to deliver high-quality, actionable LinkedIn job postings data. This approach ensures reliability and accuracy across all Canaria Inc. data offerings.