Explore the Top 50 US Tech Companies Dataset with metrics like revenue, market cap, employee size, and more. Perfect for market research, business analysis, and AI projects.

The tech industry had a rough start to 2024. Technology companies worldwide saw a significant reduction in their workforce in the first quarter of 2024, with over 57 thousand employees being laid off. By the second quarter, layoffs impacted more than 43 thousand tech employees. In the final quarter of the year around 12 thousand employees were laid off. Layoffs impacting all global tech giants Layoffs in the global market escalated dramatically in the first quarter of 2023, when the sector saw a staggering record high of 167.6 thousand employees losing their jobs. Major tech giants such as Google, Microsoft, Meta, and IBM all contributed to this figure during this quarter. Amazon, in particular, conducted the most rounds of layoffs with the highest number of employees laid off among global tech giants. Industries most affected include the consumer, hardware, food, and healthcare sectors. Notable companies that have laid off a significant number of staff include Flink, Booking.com, Uber, PayPal, LinkedIn, and Peloton, among others. Overhiring led the trend, but will AI keep it going? Layoffs in the technology sector started following an overhiring spree during the COVID-19 pandemic. Initially, companies expanded their workforce to meet increased demand for digital services during lockdowns. However, as lockdowns ended, economic uncertainties persisted and companies reevaluated their strategies, layoffs became inevitable, resulting in a record number of 263 thousand laid off employees in the global tech sector by trhe end of 2022. Moreover, it is still unclear how advancements in artificial intelligence (AI) will impact layoff trends in the tech sector. AI-driven automation can replace manual tasks leading to workforce redundancies. Whether through chatbots handling customer inquiries or predictive algorithms optimizing supply chains, the pursuit of efficiency and cost savings may result in more tech industry layoffs in the future.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

About This Dataset

This dataset provides a comprehensive record of Labor Condition Application (LCA) disclosures for H1B visa petitions filed with the U.S. Department of Labor (DOL) from 2020 to 2024. It has been cleaned and prepared for public analysis to offer valuable insights into employment trends, job categories, salaries, and geographic distribution of H1B workers.

What is H1B?

The H1B visa is a non-immigrant visa that allows U.S. companies to employ foreign workers in specialty occupations requiring theoretical or technical expertise. These roles typically include fields such as IT, engineering, finance, healthcare, and more. The H1B program is critical for addressing skill gaps in the U.S. workforce and supporting economic growth.

What is LCA?

The Labor Condition Application (LCA) is a prerequisite for filing an H1B visa petition. Employers submit the LCA to the DOL to ensure compliance with wage and working condition requirements. The LCA process protects both U.S. workers and foreign employees by enforcing: - Payment of prevailing wages. - Assurance that hiring foreign workers will not adversely affect local labor conditions.

Each LCA disclosure contains information about the employer, job title, job location, wages, and visa classification.

Why Analyze LCA Disclosure Data (2020-2024)?

The dataset spans a crucial period (2020-2024) characterized by: - Pandemic Impact: Changes in employment patterns and visa policies due to COVID-19. - Remote Work Trends: Shifts in work location dynamics for H1B visa holders. - Tech Layoffs and Restructuring: Evolving job roles and industry demands, especially in tech. - Economic Recovery: Insights into how industries and geographic regions rebounded post-pandemic.

Analyzing this data can provide: 1. Employment Trends: Discover trends in job roles, industries, and geographic locations hiring H1B workers. 2. Wage Comparisons: Compare wages across job titles, industries, and states. 3. Policy Insights: Assess the impact of government policies on foreign employment. 4. Geographic Distribution: Identify areas with the highest demand for H1B workers. 5. Industry Insights: Explore the reliance of various industries on foreign talent.

Key Features of This Dataset

• Years Covered: 2020 to 2024.
• Number of Records: Includes all LCAs filed during this period.
• Attributes: Comprehensive fields such as case number, job title, employer name, wage data, work location, and visa classification.
• Source: Data is based on public disclosures from the U.S. Department of Labor.

Who Can Use This Dataset?

• Researchers: To study the economic impact of H1B workers and labor trends.
• Policymakers: To analyze the effects of visa policies on labor markets.
• Job Seekers: To explore prevailing wages and in-demand job roles for H1B petitions.
• Businesses: To understand competitive trends in hiring foreign talent.

Higher education plays a critical role in driving an innovative economy by equipping students with knowledge and skills demanded by the workforce.While researchers and practitioners have developed data systems to track detailed occupational skills, such as those established by the U.S. Department of Labor (DOL), much less effort has been made to document which of these skills are being developed in higher education at a similar granularity.Here, we fill this gap by presenting Course-Skill Atlas -- a longitudinal dataset of skills inferred from over three million course syllabi taught at nearly three thousand U.S. higher education institutions. To construct Course-Skill Atlas, we apply natural language processing to quantify the alignment between course syllabi and detailed workplace activities (DWAs) used by the DOL to describe occupations. We then aggregate these alignment scores to create skill profiles for institutions and academic majors. Our dataset offers a large-scale representation of college education's role in preparing students for the labor market.Overall, Course-Skill Atlas can enable new research on the source of skills in the context of workforce development and provide actionable insights for shaping the future of higher education to meet evolving labor demands, especially in the face of new technologies.

Dataset Summary: This dataset analyzes layoff trends globally from 1995 to 2024, highlighting the evolution of job sectors and the influence of AI technologies on workforce dynamics. It provides insights into layoffs, reasons behind workforce changes, industry-specific impacts, and future job trends, making it a valuable resource for workforce analytics, AI adoption studies, and economic impact modeling.

Sources and Methodology: This dataset is modeled based on historical events, industry analyses, and logical extrapolations. Key data sources include:

Historical Trends:

Events like the dot-com bubble, global financial crises, and COVID-19.

Reliable sources: U.S. Bureau of Labor Statistics, World Bank, IMF Economic Outlook.

AI Trends and Projections:

Reports from McKinsey & Company, World Economic Forum, and Gartner.

Data on AI job growth and adoption: LinkedIn Economic Graphs, Crunchbase Layoff Tracker.

Skills and Future Jobs:

Reports on emerging skills and workforce trends: Future of Jobs Report 2023, TechCrunch, and Business Insider.

Projections and Logical Assumptions:

Projections for AI adoption, job creation, and displacement are based on publicly available research and extrapolation of trends.

Modeled features like "Future_Job_Trends" and "AI_Job_Percentage" combine factual data with predictive insights.

Potential Use Cases:

Economic Analysis: Study the impact of global events and technological advancements on workforce trends.

AI Adoption Trends: Explore how AI is influencing job creation and displacement across industries.

Policy Planning: Inform government and organizational policies on workforce development and reskilling.

Industry Insights: Gain insights into which industries are most affected by layoffs and which are adopting AI technologies.

Future Workforce Development: Identify emerging skills and prepare for future job market demands.

Disclaimer: This dataset is a combination of historical data, trends, and reasonable projections for future job markets influenced by AI technologies. Projections and estimates should be treated as approximations and not definitive predictions. All efforts have been made to use reliable sources and logical assumptions to ensure accuracy and usefulness for analytical purposes.

Citations:

U.S. Bureau of Labor Statistics (bls.gov)

McKinsey & Company (mckinsey.com)

World Economic Forum (weforum.org)

Gartner Reports (gartner.com)

Crunchbase Layoff Tracker (crunchbase.com)

Future of Jobs Report 2023 (weforum.org/reports)

LinkedIn Economic Graph (economicgraph.linkedin.com)

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We start by identifying U.S.-based software organizations in the computer programming and data processing industry (SIC 737), as a knowledge-intensive high-growth setting. We integrate two main data sources. First, to collect the knowledge-based measures, we use publicly available data provided by the U.S. Patent and Trademark Office (USPTO). Using the General Architecture for Text Engineering (GATE) software, we design queries that retrieve the complete class and subclass information for each patent, as well as citations, inventors, and total patents granted between 1998 and 2011 inclusive. We aggregate the data by organization-year observation at the class and subclass levels and use these aggregated measures to compute the knowledge-based predictors and covariates. To compute moving averages for some variables, we collect five years of additional USPTO data which makes our knowledge dataset span between 1993 and 2011. Second, we use Compustat to collect organization-level control variables such as assets, number of employees, market valuation, R&D expenditures, intangibles, solvency, and slack. The integration of the two datasets yields a final sample panel of 100 organizations with 3.2 years of observations on average per organization from 1998 to 2011.

The Forager.ai - US Company Dataset is a leading source of firmographic data, backed by advanced AI and offering the highest refresh rate in the industry.

| Volume and Stats |

• Over 16M total US company records, the highest volume in the industry today.
• Every company record refreshed twice a month, offering an unparalleled update frequency.
• Delivery is made every hour, ensuring you have the latest data at your fingertips.
• Each record is the result of an advanced AI-driven process, ensuring high-quality, accurate data.

| Use Cases |

Sales Platforms, ABM and Intent Data Platforms, Identity Platforms, Data Vendors:

• Map the comprehensive universe of companies globally, and acquire the latest firmographic data for all those records.
• Track company size and measure growth to determine sales propensity or to create an internal growth score.
• Enrich your existing company database with fresh, comprehensive data.

B2B Tech Companies:

• Enrich leads that sign-up through the Company Search API (available separately).
• Identify and map every company that fits your core personas and ICP.
• Build audiences to target, using key fields like location, company size, industry, and description.

Venture Capital and Private Equity:

• Discover new investment opportunities using company descriptions and industry-level data.
• Review the growth of private companies and benchmark their strength against competitors.
• Create high-level views of companies competing in popular verticals for investment.

| Delivery Options |

• Flat files via S3 or GCP
• PostgreSQL Shared Database
• PostgreSQL Managed Database
• API
• Other options available upon request, depending on the scale required

Our dataset provides a unique blend of volume, freshness, and detail that is perfect for Sales Platforms, B2B Tech, VCs & PE firms, Marketing Automation, ABM & Intent. It stands as a cornerstone in our broader data offering, ensuring you have the information you need to drive decision-making and growth.

Tags: Company Data, Company Profiles, Employee Data, Firmographic Data, AI-Driven Data, High Refresh Rate, Company Classification, Private Market Intelligence, Workforce Intelligence, Public Companies.

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   1. Integration with Broader Data Offerings: Our Technographic Install Base Data is designed to seamlessly integrate with our broader database, enabling businesses to:

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