Since the COVID-19 pandemic, the United States has experienced sharply rising then falling inflation alongside persistent labor market imbalances. This Economic Commentary interprets these macroeconomic dynamics, as represented by the Beveridge and Phillips curves, through the lens of a macroeconomic model. It uses the structure of the model to rationalize the debate about whether the US economy can expect a hard or soft landing. The model is surprised by the resiliency of the labor market as the US economy experienced disinflation. We suggest that the model’s limited ability to capture this resiliency is a feature of using a linear model to forecast the historically unprecedented movements seen after the pandemic among inflation, unemployment, and vacancy rates. We explain how, by adjusting the model to mimic congestion in a tight labor market and greater wage and price flexibility in a high-inflation environment, as during the post-pandemic period, the model can then capture what has been a path consistent with a soft landing.

Job Postings Data for Talent Acquisition, HR Strategy & Market Research Canaria’s Job Postings Data product is a structured, AI-enriched dataset that captures and organizes millions of job listings from leading sources such as Indeed, LinkedIn, and other recruiting platforms. Designed for decision-makers in HR, strategy, and research, this data reveals workforce demand trends, employer activity, and hiring signals across the U.S. labor market and enhanced with advanced enrichment models.

The dataset enables clients to track who is hiring, what roles are being posted, which skills are in demand, where talent is needed geographically, and how compensation and employment structures evolve over time. With field-level normalization and deep enrichment, it transforms noisy job listings into high-resolution labor intelligence—optimized for strategic planning, analytics, and recruiting effectiveness.

Use Cases: What This Job Postings Data Solves This enriched dataset empowers users to analyze workforce activity, employer behavior, and hiring trends across sectors, geographies, and job categories.

Talent Acquisition & HR Strategy • Identify hiring trends by industry, company, function, and geography • Optimize job listings and outreach with enriched skill, title, and seniority data • Detect companies expanding or shifting their workforce focus • Monitor new roles and emerging skills in real time

Labor Market Research & Workforce Planning • Visualize job market activity across cities, states, and ZIP codes • Analyze hiring velocity and job volume changes as macroeconomic signals • Correlate job demand with company size, sector, or compensation structure • Study occupational dynamics using AI-normalized job titles • Use directional signals (job increases/declines) to anticipate market shifts

HR Analytics & Compensation Intelligence • Map salary ranges and benefits offerings by role, location, and level • Track high-demand or hard-to-fill positions for strategic workforce planning • Support compensation planning and headcount forecasting • Feed job title normalization and metadata into internal HRIS systems • Identify talent clusters and location-based hiring inefficiencies

What Makes This Job Postings Data Unique

AI-Based Enrichment at Scale • Extracted attributes include hard skills, soft skills, certifications, and education requirements • Modeled predictions for seniority level, employment type, and remote/on-site classification • Normalized job titles using an internal taxonomy of over 50,000 unique roles • Field-level tagging ensures structured, filterable, and clean outputs

Salary Parsing & Compensation Insights • Parsed salary ranges directly from job descriptions • AI-based salary predictions for postings without explicit compensation • Compensation patterns available by job title, company, and location

Deduplication & Normalization • Achieves approximately 60% deduplication rate through semantic and metadata matching • Normalizes company names, job titles, location formats, and employment attributes • Ready-to-use, analysis-grade dataset—fully structured and cleansed

Company Matching & Metadata • Each job post is linked to a structured company profile, including metadata • Records are cross-referenced with LinkedIn and Google Maps to validate company identity and geography • Enables aggregation at employer or location level for deeper insights

Freshness & Scalability • Updated hourly to reflect real-time hiring behavior and job market shifts • Delivered in flexible formats (CSV, JSON, or data feed) and customizable filters • Supports segmentation by geography, company, seniority, salary, title, and more

Who Uses Canaria’s Job Postings Data • HR & Talent Teams – to benchmark roles, optimize pipelines, and compete for talent • Consultants & Strategy Teams – to guide clients with labor-driven insights • Market Researchers – to understand employment dynamics and job creation trends • HR Tech & SaaS Platforms – to power salary tools, job market dashboards, or recruiting features • Economic Analysts & Think Tanks – to model labor activity and hiring-based economic trends • BI & Analytics Teams – to build dashboards that track demand, skill shifts, and geographic patterns

Summary Canaria’s Job Postings Data provides an AI-enriched, clean, and analysis-ready view of the U.S. job market. Covering millions of listings from Indeed, LinkedIn, other job boards, and ATS sources, it includes detailed job attributes, inferred compensation, normalized titles, skill extraction, and employer metadata—all updated hourly and fully structured.

With deep enrichment, reliable deduplication, and company matchability, this dataset is purpose-built for users needing workforce insights, market trends, and strategic talent intelligence. Whether you're modeling skill gaps, benchmarking compensation, or visualizing hiring momentum, this dataset provides a complete toolkit for HR and labor intelligence.

About Canaria Inc. ...

Two forces are weighing on labor force growth in the United States: an aging population and recent declines in immigration. These two forces reduce the number of new jobs required to maintain a stable unemployment rate each month, known as breakeven employment growth. Lower breakeven employment growth may help contextualize recent soft payroll readings, suggesting less weakness in labor demand than payroll numbers alone might imply.

Job Skill Set

Description

The Job Skill Set Dataset is designed for use in machine learning projects related to job matching, skill extraction, and natural language processing tasks. The dataset includes detailed information about job roles, descriptions, and associated skill sets, enabling developers and researchers to build and evaluate models for career recommendation systems, resume parsing, and skill inference.

Dataset Source

This dataset was initially sourced from the Kaggle dataset titled LinkedIn Job Postings by Arshkon. The original job postings data has been enhanced by extracting skill sets using RecAI API services. These APIs are designed for skill parsing, resume analysis, and other recruitment-related tasks.

Dataset

The dataset contains the following features: - job_id: A unique identifier for each job posting. - category: The category of the job, such as INFORMATION-TECHNOLOGY,BUSINESS-DEVELOPMENT,FINANCE,SALES or HR. - job_title: The title of the job position. - job_description: A detailed text description of the job, including responsibilities and qualifications. - job_skill_set: A list of relevant skills(include hard and soft skills) associated with the job, extracted using RecAI APIs.

Use Cases

This dataset is particularly useful for the following applications:

• Skill Extraction: Identifying and parsing skills from job descriptions.
• Job-Resume Matching: Matching job descriptions with potential candidate profiles.
• Recommendation Systems: Developing models that recommend jobs or training programs based on required skills.
• Natural Language Processing: Experimenting with text-based models in recruitment and career analytics.

License

Please consult the license information on the original Kaggle dataset page here.

Citation

If you use this dataset, please cite it as follows:

@misc{batuhan_mutlu_2024,
  title={job-skill-set},
  url={https://www.kaggle.com/dsv/10201355},
  DOI={10.34740/KAGGLE/DSV/10201355},
  publisher={Kaggle},
  author={Batuhan Mutlu},
  year={2024}
}

Labour statistics by job category, for Canada, the provinces and territories, annual.

This statistic shows the revenue of the industry “manufacture of soft drinks, production of mineral waters and other bottled waters“ in Poland from 2012 to 2018, with a forecast to 2025. It is projected that the revenue of manufacture of soft drinks, production of mineral waters and other bottled waters in Poland will amount to approximately 2,026.58 million U.S. Dollars by 2025.

India Minimum Daily Wage Rate: Stone Mines: Excavation and Removal of Over Burden with 50m Lead/1.5m Lift: Soft Soil data was reported at 545.000 INR in Apr 2025. This records an increase from the previous number of 530.000 INR for Oct 2024. India Minimum Daily Wage Rate: Stone Mines: Excavation and Removal of Over Burden with 50m Lead/1.5m Lift: Soft Soil data is updated semiannually, averaging 186.080 INR from Oct 2000 (Median) to Apr 2025, with 49 observations. The data reached an all-time high of 545.000 INR in Apr 2025 and a record low of 82.260 INR in Oct 2000. India Minimum Daily Wage Rate: Stone Mines: Excavation and Removal of Over Burden with 50m Lead/1.5m Lift: Soft Soil data remains active status in CEIC and is reported by Ministry of Labour & Employment. The data is categorized under India Premium Database’s Labour Market – Table IN.GBE005: Minimum Daily Wage Rate: Chief Labour Commissioner (Central): Stone Mines.

Career Simulation Platforms Market Outlook

According to our latest research, the global career simulation platforms market size reached USD 2.14 billion in 2024, with a robust compound annual growth rate (CAGR) of 15.2% projected through the forecast period. By 2033, the market is anticipated to attain a value of USD 6.18 billion. This significant growth trajectory is primarily fueled by the increasing demand for immersive and interactive career exploration tools, the rapid digitization of education and corporate training, and the growing emphasis on workforce readiness and upskilling across diverse industries.

One of the primary growth drivers in the career simulation platforms market is the global shift towards experiential learning and skills-based education. As traditional pedagogical methods face scrutiny for their ability to prepare learners for real-world challenges, institutions and enterprises are increasingly adopting simulation-based platforms to bridge the gap between theory and practical application. These platforms enable users to engage in realistic job scenarios, fostering critical thinking, decision-making, and problem-solving skills. The adoption is further accelerated by the rise of remote and hybrid learning models, which necessitate digital solutions capable of delivering high engagement and measurable outcomes. As a result, educational institutions, corporate training departments, and career counselors are investing heavily in career simulation technology to enhance employability and workforce performance.

Another significant factor contributing to the market's expansion is the integration of advanced technologies such as artificial intelligence, machine learning, and virtual reality within career simulation platforms. These innovations have revolutionized the user experience by providing personalized learning paths, real-time feedback, and adaptive simulations that mimic authentic workplace environments. AI-driven analytics allow educators and HR professionals to track progress, identify skill gaps, and tailor interventions, thereby improving the overall effectiveness of training programs. The continuous evolution of these technologies, combined with declining hardware costs and the growing availability of high-speed internet, is making career simulation platforms more accessible and affordable for organizations of all sizes.

Moreover, the increasing focus on workforce development and lifelong learning is driving demand for career simulation platforms across multiple sectors. Governments and enterprises alike recognize the importance of reskilling and upskilling to remain competitive in a rapidly changing job market. Initiatives aimed at promoting STEM education, digital literacy, and soft skills development are leveraging simulation platforms to deliver engaging and scalable training solutions. This trend is particularly pronounced in regions grappling with high youth unemployment and skills mismatches, where simulation-based learning offers a practical approach to career counseling and job readiness. As the market matures, we expect to see a proliferation of specialized platforms catering to niche industries and job roles, further fueling growth.

From a regional perspective, North America continues to dominate the career simulation platforms market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The strong presence of leading technology vendors, high adoption rates among educational institutions and corporate enterprises, and supportive government policies contribute to North America's leadership position. Meanwhile, the Asia Pacific region is witnessing the fastest growth, driven by rapid digital transformation, expanding internet penetration, and increasing investments in education technology. Latin America and the Middle East & Africa are also emerging as promising markets, supported by efforts to modernize educational infrastructure and address workforce development challenges.

Component Analysis

The component segment of the career simulation platforms market is primarily divided into software and services. The software component encompasses the core simulation platforms, which include customizable modules, interactive interfaces, and integration capabilities with other learning management systems. Software solutions are at the heart of the market, driving innovation through advanced features such as A

Spain Job Offers Scraped Data

Uncovering Qualifications and Requirements

By [source]

About this dataset

This dataset contains valuable web scraping information about job offers located in Spain, and gives details such as the offer name, company, location, and time of offer to potential employers. Having this knowledge is incredibly beneficial for any job seeker looking to target potential employers in Spain, understand the qualifications and requirements needed to be considered for a role and know approximately how long an offer is likely to stay on Linkedin. This dataset can also be extremely useful for recruiters who need a detailed overview of all job offers currently active in the Spanish market in order to filter out relevant vacancies. Lastly, professionals who have an eye on the Spanish job market can especially benefit from this dataset as it provides useful insights that can help optimise their search even more. This dataset consequently makes it easy for users interested in uncovering opportunities within Spain’s labour landscape with access detailed information about current job opportunities at their fingertips

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For more datasets, click here.

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How to use the dataset

This guide will help those looking to use this dataset to discover the job market in Spain. The data provided in the dataset can be a great starting point for people who want to optimize their job search and uncover potential opportunities available.

• Understand What Is Being Measured:The dataset contains details such as a job offer name, company, and location along with other factors such as time of offer and type of schedule asked. It is important to understand what each column represents before using the data set.
• Number of Job Offers Available:This dataset provides an insight on how many job offers are available throughout Spain by showing which areas have a high number of jobs listed and what types of jobs are needed in certain areas or businesses. This information could be used for expanding your career or for searching for specific jobs within different regions in Spain that match your skillset or desired salary range .
• Required Qualifications & Skill Set:The type of schedule being asked by businesses is also mentioned, allowing users to understand if certain employers require multiple shifts, weekend work or hours outside the normal 9 - 5 depending on positions needed within companies located throughout the country . Additionally, understanding what skills sets are required not only quality you prioritize when learning new technologies or gaining qualifications but can give you an idea about what other soft skills may be required by businesses like team work , communication etc..
• Location Opportunities:This web scraping list allows users to gain access into potential companies located throughout Spain such as Madrid , Barcelona , Valencia etc.. By understanding where business demand exists across different regions one could look at taking up new roles with higher remuneration , specialize more closely in recruitments/searches tailored specifically towards various regions around Spain .

By following this guide, you should now have a robust understanding about how best utilize this dataset obtained from UOC along with an increased knowledge on identifying job opportunities available through webscraping for those seeking work experience/positions across multiple regions within the country

Research Ideas

• Analyzing the job market in Spain - Companies offering jobs can be compared and contrasted using this dataset, such as locations of where they are looking to hire, types of schedules they offer, length of job postings, etc. This information can let users to target potential employers instead of wasting time randomly applying for jobs online.
• Optimizing a Job Search- Web scraping allows users to quickly gather job postings from all sources on a daily basis and view relevant qualifications and requirements needed for each post in order to better optimize their job search process.
• Leveraging data insights – Insights collected by analyzing this web scraping dataset can be used for strategic advantage when creating LinkedIn or recruitment campaigns targeting Spanish markets based on the available applicants’ preferences – such as hours per week or area/position within particular companies typically offered in the datas set available from UOC

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

L...

This powerful dataset represents a meticulously curated snapshot of the United States job market throughout 2021, sourced directly from CareerBuilder, a venerable employment website founded in 1995 with a formidable global footprint spanning the US, Canada, Europe, and Asia. It offers an unparalleled opportunity for in-depth research and strategic analysis.

Dataset Specifications:

• Source: CareerBuilder.com (US Listings)
• Crawled by: Crawl Feeds in-house team
• Volume: Over 422,000 unique job records
• Timeliness: Last crawled in May 2021, providing a critical historical benchmark for post-pandemic labor market recovery and shifts.
• Format: Compressed ZIP archive containing structured JSON files, designed for seamless integration into databases, analytical platforms, and machine learning pipelines.
• Accessibility: Published and available immediately for acquisition.

Richness of Detail (22 Comprehensive Fields):

The true analytical power of this dataset stems from its 22 granular data points per job listing, offering a multi-faceted view of each employment opportunity:

1. Core Job & Role Information:

   • id: A unique, immutable identifier for each job posting.
   • title: The specific job role (e.g., "Software Engineer," "Marketing Manager").
   • description: A condensed summary of the role, responsibilities, and key requirements.
   • raw_description: The complete, unformatted HTML/text content of the original job posting – invaluable for advanced Natural Language Processing (NLP) and deeper textual analysis.
   • posted_at: The precise date and time the job was published, enabling trend analysis over daily or weekly periods.
   • employment_type: Clarifies the nature of the role (e.g., "Full-time," "Part-time," "Contract," "Temporary").
   • url: The direct link back to the original job posting on CareerBuilder, allowing for contextual validation or deeper exploration.

2. Compensation & Professional Experience:

   • salary: Numeric ranges or discrete values indicating the compensation offered, crucial for salary benchmarking and compensation strategy.
   • experience: Specifies the level of professional experience required (e.g., "Entry-level," "Mid-senior level," "Executive").

3. Organizational & Sector Context:

   • company: The name of the employer, essential for company-specific analysis, competitive intelligence, and brand reputation studies.
   • domain: Categorizes the job within broader industry sectors or functional areas, facilitating industry-specific talent analysis.

4. Skills & Educational Requirements:

   • skills: A rich collection of keywords, phrases, or structured tags representing the specific technical, soft, or industry-specific skills sought by employers. Ideal for identifying skill gaps and emerging skill demands.
   • education: Outlines the minimum or preferred educational qualifications (e.g., "Bachelor's Degree," "Master's Degree," "High School Diploma").

5. Precise Geographic & Location Data:

   • country: Specifies the country (United States for this dataset).
   • region: The state or province where the job is located.
   • locality: The city or town of the job.
   • address: The specific street address of the workplace (if provided), enabling highly localized analysis.
   • location: A more generalized location string often provided by the job board.
   • postalcode: The exact postal code, allowing for granular geographic clustering and demographic overlay.
   • latitude & longitude: Geospatial coordinates for precise mapping, heatmaps, and proximity analysis.

6. Crawling Metadata:

   • crawled_at: The exact timestamp when each individual record was acquired, vital for understanding data freshness and chronological analysis of changes.

Expanded Use Cases & Analytical Applications:

This comprehensive dataset empowers a wide array of research and commercial applications:

• Deep Labor Market Trend Analysis:

  • Identify the most in-demand job titles, skills, and educational backgrounds across different US regions and industries in 2021.
  • Analyze month-over-month or quarter-over-quarter hiring trends to understand recovery patterns or shifts in specific sectors post-pandemic.
  • Spot emerging job roles or skill combinations that gained prominence during the dataset's period.
  • Assess the volume of remote vs. in-person job postings and their distribution.

• Strategic Talent Acquisition & HR Analytics:

  • Benchmark job requirements, salary ranges, and desired experience levels against market averages for specific roles.
  • Optimize job descriptions by identifying common keywords and phrases used by top employers for similar positions.
  • Understand the competitive landscape for talent in specific geographic areas or specialized skill sets.
  • Develop data-driven recruitment strategies by identifying where and how competitors are hiring.

• Compensation & Benefits Research:

  • Conduct detailed salary analysis broken down by job title, industry, location (state, city, even postal code), experience level, and required skills.
  • Identify potential salary premiums or discrepancies for niche skills or hard-to-fill roles.
  • Support robust compensation planning and negotiation strategies.

• Educational & Workforce Development Planning:

  • Universities and vocational schools can align curriculum with real-world employer demand by analyzing required skills and education fields.
  • Government agencies can identify areas for workforce retraining or development programs based on skill gaps revealed in job postings.
  • Career counselors can advise job seekers on in-demand skills and promising career paths.

• Economic Research & Forecasting:

  • Economists can use the volume and nature of job postings as a leading indicator for economic activity and regional growth.
  • Analyze the impact of economic policies or global events on specific industries' hiring patterns.
  • Study labor mobility and migration patterns based on job locations.

• Competitive Intelligence for Businesses:

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India Minimum Daily Wage Rate: Stone Mines: Excavation and Removal of Over Burden with 50m Lead/1.5m Lift: Soft Soil with Rock data was reported at 818.000 INR in Apr 2025. This records an increase from the previous number of 795.000 INR for Oct 2024. India Minimum Daily Wage Rate: Stone Mines: Excavation and Removal of Over Burden with 50m Lead/1.5m Lift: Soft Soil with Rock data is updated semiannually, averaging 281.580 INR from Oct 2000 (Median) to Apr 2025, with 49 observations. The data reached an all-time high of 818.000 INR in Apr 2025 and a record low of 125.340 INR in Oct 2000. India Minimum Daily Wage Rate: Stone Mines: Excavation and Removal of Over Burden with 50m Lead/1.5m Lift: Soft Soil with Rock data remains active status in CEIC and is reported by Ministry of Labour & Employment. The data is categorized under India Premium Database’s Labour Market – Table IN.GBE005: Minimum Daily Wage Rate: Chief Labour Commissioner (Central): Stone Mines.

India Minimum Daily Wage Rate: Stone Mines: Excavation and Removal of Over Burden with 50m Lead/1.5m Lift: Soft Soil with Rock: Basic data was reported at 531.000 INR in Apr 2025. This stayed constant from the previous number of 531.000 INR for Oct 2024. India Minimum Daily Wage Rate: Stone Mines: Excavation and Removal of Over Burden with 50m Lead/1.5m Lift: Soft Soil with Rock: Basic data is updated semiannually, averaging 157.780 INR from Oct 2000 (Median) to Apr 2025, with 49 observations. The data reached an all-time high of 531.000 INR in Apr 2025 and a record low of 99.000 INR in Oct 2005. India Minimum Daily Wage Rate: Stone Mines: Excavation and Removal of Over Burden with 50m Lead/1.5m Lift: Soft Soil with Rock: Basic data remains active status in CEIC and is reported by Ministry of Labour & Employment. The data is categorized under India Premium Database’s Labour Market – Table IN.GBE005: Minimum Daily Wage Rate: Chief Labour Commissioner (Central): Stone Mines.

IntroductionThe rise of emerging public health threats has increased the need for qualified epidemiologists in Canada. Our study aimed to identify the knowledge, skills, and abilities (KSAs) required of epidemiologists entering the workforce and determine whether these align with those taught in graduate epidemiology programs.MethodsAn inductive content analysis of Canadian job postings from May to December 2023 containing the keyword “epidemiology” and requiring master’s degrees in epidemiology or related fields was conducted to identify the KSAs required in the workforce. Inductive content analysis of Master of Science (MSc) program descriptions and core course descriptions was completed to discern skills gained through Canadian graduate epidemiology and public health programs.ResultsBased on the 295 job postings analyzed, five KSA categories were identified: communication skills (n = 268, 90.8%), analytical skills (n = 267, 90.5%), soft skills (n = 254, 86.1%), research methodology (n = 217, 73.6%), and knowledge of epidemiological concepts (n = 170, 57.6%). Analysis of 18 MSc programs found that that all of them described analytical skills, research methodology, and epidemiological concepts within their curriculum. Communication skills were described in 94.4% (n = 17) of programs, while soft skills were mentioned in 50.0% (n = 9). However, only 66.7% (n = 12) of programs outlined learning objectives or specified the skills acquired from their programs in their descriptions.ConclusionThere was alignment between the needs of the Canadian epidemiology job market and MSc programs, particularly in analytical skills and research methodology. However, development of soft skills should be emphasized within graduate epidemiology programs to better prepare graduates for the job market. Future research should aim to develop competency statements for epidemiologists in training to ensure consistency across graduate programs and promote career readiness.

The database is one of the results of the project "Metropolitan Governance in Spain: Institutionalization and Models" (METROGOV, 2020-23), funded by the National R&D Plan 2019 of the Ministry of Science and Innovation (PID2019-106931GA-I00). Directed by Professor Tomàs, the project wants to understand the building and definition of models of metropolitan governance in Spain. There is no comprehensive work based on a common methodology that address this topic, this is why the METROGOV project seeks to cover this gap in the literature and the research.

The first specific goal of the project was to create a database of metropolitan institutions in Spain, including hard forms like metropolitan governments, metropolitan sectorial agencies and consortiums as well as soft forms as metropolitan strategic plans. The database provides an updated and rigorous portrait of the institutional thickness of urban agglomerations, gathering up to 384 metropolitan cooperation instruments in the Spanish functional areas. In other words, it is a picture of the institutional reality of Spanish urban agglomerations. This database provides precious information about the model of metropolitan governance, the municipalities involved and the sectors with most and less institutionalization.

As in Spain there is not an official or statistical definition of metropolitan areas, the project departed from the concept of Functional Urban Areas (FUA), considered as “densely inhabited city and a less densely populated commuting zone whose labour market is highly integrated with the city” (Eurostat). According to this definition, the commuting zone contains the surrounding travel-to-work areas of a city where at least 15 % of employed residents are working in a city. In the case of Spain, we find 45 big FUA, where the central city has more than 100.000 inhabitants. The database was structured considering these 45 Spanish FUAs, and it was necessary that at least 3 municipalities participated in the metropolitan cooperation tools.

In the grid, you will find the 384 instruments of metropolitan cooperation following different criteria. First of all, the models of metropolitan governance, from hard to soft: metropolitan government, metropolitan sectoral agency, “mancomunidad”, consortium, public or public-private company, territorial plan, sectoral plan, comarca, association of municipalities, strategic plan, European project, working group. Each instrument is also classified according to the subject of cooperation: transport, waste, water, housing, urbanism, etc. Other complementary information is added, such as: the year of creation; number and names of municipalities that are part of the entity; percentage of territory covered by this tool, etc.

A book has been recently published with the results of the project: Tomàs, M. (2023) (ed.). Metrópolis sin gobierno. La anomalía española en Europa. València: Tirant lo Blanch.

Skill Taxonomy Data US | AI-Powered Title & Skill Taxonomy Matched with Job Postings for Talent Intelligence & Workforce Planning

Our Skill Taxonomy Data product offers a comprehensive, AI-driven hierarchical mapping of job titles and associated skills, certifications, and qualifications — precisely trained 600M+ job postings across the US labor market. This data empowers HR professionals, talent managers, and workforce planners with unparalleled insights for strategic decision-making.

Harnessing state-of-the-art AI and large language models (LLMs) validated by human experts, our skill taxonomy data surpasses traditional keyword matching by leveraging advanced entity recognition, contextual filtering, and skill relevance ranking. This ensures highly accurate, actionable intelligence for talent acquisition, HR analytics, and workforce development.

What the Skill Taxonomy Data Solves Our data transforms unstructured job market information into a dynamic, normalized taxonomy of skills and titles — addressing key questions such as:

• What specific hard and soft skills are demanded across job titles and industries? • How do certifications and qualifications align with workforce requirements? • Which skills are emerging or declining in demand for targeted workforce planning? • How can organizations benchmark and close skill gaps effectively? • What hierarchical skill relationships support career development and succession planning?

Key Features & Capabilities Normalized Titles & Skills • ~70,000 unique normalized job titles (e.g., Human Resources Generalist) • Up to 20 hard skills per title (~37,000 unique skills, e.g., Python) • Up to 10 soft skills per title (~400 unique, e.g., Communication) • ~3,000 unique certifications (e.g., PMP) and ~8 standardized qualification levels (e.g., Bachelor’s) • Weighted relevance scores based on frequency, uniqueness, and contextual significance

Advanced AI & NLP Enrichment • Contextual entity recognition using NER, embeddings, MinHash, and AI-based filtering • Disambiguation of skill variants (e.g., ML → Machine Learning) • Exclusion of irrelevant entities based on job role context

Hierarchical Skill Mapping & Descriptions • Structured taxonomy from broad categories to granular capabilities • Clear skill definitions for use in HR, L&D, and employee development

Demand & Supply Analytics • Real-time and historical tracking of skill demand from 600M+ job postings • Visibility into skill clusters, their market value, and projected workforce needs

Interactive Search & Insights • Searchable skill-to-title mapping for recruitment and internal mobility • Support for targeted training programs and career pathing initiatives

Continuous Updates & Market Responsiveness • Monthly data refreshes reflecting changes in the labor market and evolving tech landscape

Comprehensive Workforce Management Support • Actionable insights to optimize hiring, reskill existing employees, and plan succession effectively

Scalable & Customizable Solutions • Adaptable across industries and organization sizes • Customizable to support bespoke strategic HR and analytics initiatives

Use Cases & Benefits • Human Resources (HR): Streamline recruitment by identifying critical role-specific skills and certifications • Talent Management: Design L&D programs aligned with in-demand and emerging skills • HR Consulting: Deliver evidence-based talent diagnostics and strategic workforce solutions • Human Resources Planning: Forecast and prepare for evolving organizational skill needs • HR Analytics: Detect and analyze skill trends for better workforce and talent decisions

What Makes This Skill Taxonomy Data Unique • AI-Validated Skill Recognition: Goes beyond keyword matching using contextual LLMs and AI models • Data Depth & Breadth: Covers over 600M US job postings across industries, refreshed monthly • Weighted Skill Importance: Context-aware scoring system suppresses generic skills and highlights role-specific needs • Rich Metadata: Includes weights, skill definitions, certifications, and qualifications — integrated into a structured hierarchy • Seamless Integration: Easily embedded into HRIS, ATS, L&D, and workforce analytics tools

Who Uses Our Skill Taxonomy Data • HR & Talent Acquisition Teams – For sourcing, screening, and candidate-job matching • Learning & Development Managers – For designing targeted training and upskilling programs • Workforce Planning & Analytics Teams – To anticipate future hiring and skill needs • HR Consultants & Analysts – For delivering actionable talent strategy and diagnostics • Business Leaders & Strategy Teams – To inform competitive workforce and organizational strategies

About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, and Glassdoor salary analytics. We deliver clean, structured, and enriched datasets at scale using proprietary data scraping pipelines and advanced AI/LLM-based modeling...

Professional Skill Training Market Outlook

The global professional skill training market size was valued at approximately $92.3 billion in 2023 and is projected to reach around $148.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 5.4% during the forecast period. This robust growth is driven by the increasing need for continuous learning and upskilling in an ever-evolving job market. Factors such as technological advancements, globalization, and the rapid pace of innovation across various industries necessitate ongoing professional development, making skill training indispensable for both individuals and organizations.

One of the primary growth factors for the professional skill training market is the accelerated pace of technological change. With the advent of digital transformation, technologies such as artificial intelligence, machine learning, and blockchain are reshaping industries and job roles. Consequently, there is a growing emphasis on technical skill training to bridge the gap between current workforce capabilities and future job requirements. Companies are investing heavily in training programs to enhance the technical proficiency of their employees, which in turn is driving market growth.

Another significant growth driver is the increasing importance of soft skills in the workplace. As automation and artificial intelligence take over routine tasks, the demand for human-centric skills such as emotional intelligence, communication, leadership, and teamwork is on the rise. Organizations recognize that soft skills are critical for improving workplace productivity, fostering innovation, and maintaining a competitive edge. This has led to a surge in demand for soft skill training programs, further propelling the growth of the professional skill training market.

The proliferation of remote work and the gig economy is also contributing to the expansion of the professional skill training market. With more people working remotely or as freelancers, there is a heightened need for flexible and accessible training solutions that can be undertaken from anywhere. Online and blended learning platforms are becoming increasingly popular, allowing individuals to upgrade their skills at their own pace and convenience. This shift towards flexible learning models is expected to continue driving market growth in the coming years.

Vocational Training plays a pivotal role in the professional skill training market by providing practical, job-specific skills that are essential for various trades and industries. Unlike traditional academic education, vocational training focuses on equipping learners with hands-on experience and technical expertise tailored to specific career paths. This type of training is particularly beneficial in sectors such as manufacturing, healthcare, and information technology, where specialized skills are in high demand. As the job market continues to evolve, vocational training offers a viable pathway for individuals seeking to enhance their employability and adapt to changing industry requirements. By bridging the gap between theoretical knowledge and practical application, vocational training contributes significantly to workforce development and economic growth.

Regionally, North America holds a significant share in the professional skill training market, driven by a strong emphasis on continuous learning and development, particularly in the corporate sector. Europe follows closely, with substantial investments in employee training and development initiatives across various industries. The Asia Pacific region is expected to witness the fastest growth, owing to the rapid economic development, increasing workforce, and rising adoption of digital learning solutions. Latin America and the Middle East & Africa are also showing promising growth trends, albeit at a relatively slower pace compared to other regions.

Training Type Analysis

The professional skill training market is segmented by training type, which includes Technical Skills, Soft Skills, Management Skills, Language Skills, and Others. Technical skills training is anticipated to dominate the market due to the rapid technological advancements and the growing need for specialized knowledge in areas such as cybersecurity, data science, and software development. Companies are increasingly realizing the importance of equipping t

LinkedIn is a widely used professional networking platform that hosts millions of job postings. This dataset contains 1.3 million job listings scraped from LinkedIn in the year 2024.

This dataset can be used for various research tasks such as job market analysis, skills mapping, job recommendation systems, and more.

If you find this dataset valuable, please upvote 😊💼

This is the same master dataset that powers SkillExplorer

Interesting Task Ideas:

1. Practice data cleaning on raw data.
2. Analyze the most in-demand job titles or industries in different cities or countries.
3. Identify the top companies hiring for specific job positions.
4. Utilize the skills data to determine the most sought-after skills in different job categories.
5. Build a job recommendation system based on user profiles and job listing data.
6. Discover patterns in job types or levels across different industries.
7. Identify skill gaps in the job market to inform educational or training programs.
8. Explore the relationship between job title and required skills.

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The global job simulation test market for recruitment is experiencing robust growth, driven by the increasing need for organizations to assess candidates' practical skills and soft competencies more effectively. The shift towards a skills-based hiring approach, coupled with the limitations of traditional resume screening and interviews, fuels the demand for realistic job simulations. This market is segmented by application (SMEs and large enterprises) and test type (role-playing, situational judgment, group testing, and others). Large enterprises, with their greater resources and higher hiring volumes, currently dominate the market, although the SME segment is experiencing rapid growth as cost-effective solutions become more accessible. The prevalence of role-playing and situational judgment tests highlights the importance of evaluating candidate behavior and decision-making in simulated work environments. Technological advancements in test delivery platforms, data analytics, and AI-powered assessment tools further enhance the market's appeal, enabling organizations to gain deeper insights into candidate suitability. Geographic distribution sees North America and Europe as leading regions, reflecting established HR practices and high adoption rates of advanced technologies. However, significant growth opportunities exist in Asia-Pacific, particularly in India and China, due to burgeoning economies and increasing digitalization in the recruitment sector. While data privacy concerns and the potential for bias in assessment algorithms represent challenges, the overall market trajectory points towards sustained and accelerated growth over the next decade. The competitive landscape is characterized by a mix of established players and emerging startups. Established players like Journeyfront and Forage offer comprehensive platforms with a range of simulation types, while newer entrants like TestGorilla and Adaface focus on specific niches or innovative assessment methods. The market is likely to see further consolidation and innovation as companies strive to differentiate their offerings and cater to the evolving needs of recruiters. The projected CAGR suggests a considerable increase in market value over the forecast period (2025-2033), driven by factors such as increased investment in HR technology, expanding adoption across various industries, and the growing recognition of the effectiveness of job simulation tests in identifying top talent. Continued focus on improving test design, ensuring fairness and avoiding bias, will be crucial for sustained growth and market acceptance.