Unlock valuable salary insights with our comprehensive Salary Dataset, designed for businesses, recruiters, and job seekers to analyze compensation trends, workforce planning, and market competitiveness.

Dataset Features

Job Listings & Salaries: Access structured salary data from top job platforms, including job titles, company names, locations, salary ranges, and compensation types. Employer & Industry Insights: Extract company-specific salary trends, industry benchmarks, and hiring patterns. Geographic Pay Disparities: Compare salaries across different regions, cities, and countries to identify location-based compensation trends. Job Market Trends: Monitor salary fluctuations, demand for specific roles, and hiring trends over time.

Customizable Subsets for Specific Needs Our Salary Dataset is fully customizable, allowing you to filter data based on job titles, industries, locations, experience levels, and salary ranges. Whether you need broad market insights or focused data for recruitment strategy, we tailor the dataset to your needs.

Popular Use Cases

Workforce Planning & Talent Acquisition: Optimize hiring strategies by analyzing salary benchmarks and compensation trends. Market Research & Competitive Intelligence: Compare salaries across industries and competitors to stay ahead in talent acquisition. Career Decision-Making: Help job seekers evaluate salary expectations and identify high-paying opportunities. AI & Predictive Analytics: Use structured salary data to train AI models for job market forecasting and compensation analysis. Geographic Expansion & Business Strategy: Assess salary variations across regions to plan business expansions and remote workforce strategies.

Whether you're optimizing recruitment, analyzing salary trends, or making data-driven career decisions, our Salary Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

Wages in the United States increased 4.78 percent in June of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Wages and Salaries Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Analyzing Average Wage Rates by Occupation in the USA" provides a comprehensive dataset and analysis of average wage rates across various occupations in the United States. This dataset offers valuable insights into the income patterns and salary trends prevalent in different job categories, allowing researchers, policymakers, and individuals to better understand the earning potential in specific occupations. By examining this dataset, one can gain a deeper understanding of the variations in wages across industries, professions, and skill levels, ultimately aiding in informed decision-making and strategic career planning. Description: ChatGPT

What are data science professionals really earning in 2025? This dataset offers a comprehensive look into global salary trends for roles in Data Science, Machine Learning, and Artificial Intelligence.

Carefully curated using a combination of market research and publicly available data sources—including the AIJobs salary survey (CC0 license), 365DataScience, Payscale, KDnuggets, ZipRecruiter, and others—this dataset reflects real-world compensation patterns from around the globe.

Why This Dataset Matters

Whether you're a data scientist, AI practitioner, student, recruiter, or industry researcher, this dataset is built to support:

• Salary prediction and ML modeling
• Global market benchmarking
• Career decision-making and negotiation
• Remote work trend analysis
• Business intelligence dashboards and visualizations

Data Sources

• aijobs.net Salary Dataset (CC0)
• 365datascience.com
• Payscale
• KDnuggets
• ZipRecruiter
• Wellfound (AngelList)

Envestnet | Yodlee's Payroll Data Panel captures de-identified payroll information to deliver valuable employment insights, such as a company's wage costs, seasonal performance, headcount, hiring, layoffs, and more.

De-identified payroll data analytics for major employers gives decision makers insight into employment trends across many industries. The payroll product includes 1000+ employers and data can be used for company specific or macro purposes. - 4800+ employers tagged - Frequency of payroll identified (i.e. weekly, bi-weekly)
- Data at user and account level to allow for cohort analysis (e.g. Macys likely to lose 10% of revenue due to unemployment within their cohort)

New Features - Mapping to Category codes and Employer Dependency Scoring Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Data Science Dashboard

** Exciting Data Science Dashboard Project Overview **

I'm thrilled to share my latest Power BI dashboard project focusing on the salaries and employment trends of Data Science professionals from 2020 to 2022. Dive into the details of this insightful dashboard that sheds light on various aspects of employee demographics, compensation, and work trends.

** Detailed Visualizations: **

Total Employee Count: Track the growth of the Data Science team over the three-year period. Average Salary Analysis: Explore the average salaries for each year (2020, 2021, and 2022) to identify trends and fluctuations. Company Size Distribution: Visualize the distribution of employees across small, medium, and largesized companies where Data Science professionals worked. Salary Distribution by Job Titles: Gain insights into salary distribution across different job titles through a clustered bar chart. Experience Level and Employment Type: Analyze the distribution of employees based on experience levels and employment types through an informative table. Remote Work Ratio: Understand the proportion of remote work over time through a stacked area chart, correlated with salary levels in USD. Interactive Slicers: Use drop-down slicers for job titles and work years to customize your data exploration experience. **Key Takeaways and Insights: **

Identify hiring trends and patterns in the Data Science field over the three-year period. Understand salary distributions based on job titles and experience levels. Gain insights into the prevalence of remote work and its correlation with salary levels. Explore the impact of company size on employment opportunities and compensation. ** Unlocking Insights for Decision-Making: **

This Power BI dashboard provides valuable insights for HR professionals, hiring managers, and Data Science enthusiasts alike. Use the interactive features to drill down into specific segments and extract actionable insights for strategic decision-making. Leverage the visualizations to inform recruitment strategies, salary negotiations, and workforce planning initiatives. **Ready to Explore the Future of Data Science Employment? **

Dive into this comprehensive dashboard to uncover trends, patterns, and insights that drive the Data Science industry forward. Let's connect to discuss how these insights can inform your business strategies and propel your organization towards success.

The Payroll & Compensation Management Market report segments the industry into Type (Software, Services), Application (Payroll, Employee Benefits, and more), Deployment (On-Premises Deployment, Cloud Hosted Deployment), End-User Industry (BFSI, Retail, and more), and Region (North America, Europe, and more).

The global Salary Benchmarking Software market is anticipated to exhibit a robust growth trajectory, reaching a market size of approximately USD 1,500 million by 2033. This growth is driven by an increasing need for organizations to manage compensation and benefits packages effectively and ensure competitive remuneration strategies. Key market drivers include rising labor costs, global talent shortages, and the increasing complexity of compensation structures. Moreover, the adoption of cloud-based solutions and the need for real-time data insights are further fueling market growth. The market is fragmented, with a mix of established players and emerging entrants. Key companies include MarketPay, Compensation Tool, Ravio, Barley, OpenComp, Figures, PayReview, Arcoro, Carta Total Comp, Compease, compensly, Decusoft, Compport, Economic Research Institute, Horsefly, HRSoft, Pave, Salary Expert, Soderberg & Partners, SupportFinity, and Workday. Strategic partnerships, acquisitions, and product innovations are expected to shape the competitive landscape in the coming years. Market growth is anticipated to be concentrated in North America and Europe, followed by the Middle East and Africa and the Asia Pacific region.

Introducing the Dataset: Data Science Salary Trends 2023

This dataset aims to shed light on the salary trends in the field of Data Science for the years 2021 to 2023. With a focus on various aspects of employment, including work experience, job titles, and company locations, this dataset provides valuable insights into salary distributions within the industry.

Data Fields: - work_year: Representing the specific year of salary data collection. - Experience_level: The level of work experience of the employees, categorized as EN (Entry-Level), EX (Experienced), MI (Mid-Level), SE (Senior). - Employment_type: The type of employment, labelled as FT (Full-Time), CT (Contractor), FL (Freelancer), PT (Part-Time). - Job_title: The job titles of the employees, such as "Applied Scientist", "Data Quality Analyst" , etc. - Salary: The salary figures in their respective currency formats. - Salary_currency: The currency code representing the salary. - Salary_in_usd: The converted salary figures in USD for uniform comparison. - Company_location: The location of the companies, specified as country codes (e.g., "US" for the United States and "NG" for Nigeria). - Company_size: The size of the companies, classified as "L" (Large), "M" (Medium), and "S" (Small).

With this dataset, data enthusiasts and analysts can delve into the salary dynamics of Data Science professionals in 2023, identifying trends across different experience levels, job titles, and company sizes. It can be a valuable resource for understanding the economic landscape in the Data Science job market and making informed decisions for both job seekers and employers alike.

**Potential Problem Statements. ** 1. Optimal Hiring Decisions: Analyze the dataset to determine the best employment type and experience level for hiring data science professionals for maximum cost-effectiveness. 2. Salary Trends over Time: Utilize the dataset to visualize and interpret data science salary trends from 2021 to 2023. 3. Job Title Recommendation: Recommend suitable job titles for candidates based on their experience level and desired salary range.

Kindly, upvote if you find the dataset interesting. Thank you.

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

The National Compensation Survey (NCS) provides comprehensive measures of occupational wages; employment cost trends, and benefit incidence and detailed plan provisions. Detailed occupational earnings are available for metropolitan and non-metropolitan areas, broad geographic regions, and on a national basis. The index component of the NCS (ECI) measures changes in labor costs. Average hourly employer cost for employee compensation is presented in the ECEC.

Snapshot img

Compensation Software Market Size 2025-2029

The compensation software market size is forecast to increase by USD 7.83 billion, at a CAGR of 11.6% between 2024 and 2029.

The market is driven by the increasing adoption of pricing strategies by companies in response to the growing demand for integrated Human Capital Management (HCM) solutions. This trend reflects the evolving needs of businesses seeking to streamline their HR processes and gain a competitive edge through data-driven compensation decisions. However, high implementation and maintenance costs pose a significant challenge for market participants. These expenses can deter smaller organizations and limit the market's growth potential. To capitalize on opportunities and navigate challenges effectively, companies must focus on offering cost-effective solutions while maintaining the necessary functionality and integration capabilities.
By addressing the cost concern, companies can expand their customer base and strengthen their market position. Additionally, continuous innovation and investment in technology will be crucial to meet the evolving demands of businesses and maintain a competitive edge.

What will be the Size of the Compensation Software Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by the dynamic nature of business environments and the need for innovative solutions. Seamlessly integrated offerings, such as API integrations, machine learning, reporting and dashboards, HRS integration, deferred compensation, compensation procedures, variable pay, compensation reviews, real-time data, compensation design, security and compliance, incentive programs, mobile compensation, and performance management, are essential for organizations to effectively manage their talent and rewards strategies. Compensation software solutions are increasingly being adopted across various sectors to streamline processes, ensure compliance, and optimize costs. These solutions enable organizations to design and implement compensation strategies, policies, and philosophies that align with their business objectives.

Data visualization and analytics are critical components of compensation software, providing valuable insights into compensation trends and patterns. Machine learning algorithms and predictive analytics enable organizations to make data-driven decisions, optimize compensation structures, and retain top talent. Security and compliance are paramount in the market. Solutions must adhere to the latest regulations and standards to ensure data privacy and security. Integrations with HRIS, payroll, benefits administration, and performance management systems further enhance the functionality of compensation software. Compensation software solutions offer user-friendly interfaces, enabling easy access to critical information. Real-time data and automated workflows enable organizations to make timely compensation adjustments and respond to market changes.

Incentive programs, bonuses, and performance-based pay are essential components of compensation software, enabling organizations to align employee compensation with performance and business objectives. Employee engagement, satisfaction, and development are also key areas of focus, with solutions offering training, career pathing, and communication tools. Budgeting and forecasting capabilities enable organizations to optimize costs and plan for future compensation needs. Cloud-based solutions offer flexibility and scalability, while workflow automation streamlines processes and improves efficiency. Compensation software solutions continue to evolve, with new features and capabilities emerging to meet the changing needs of organizations. The market is expected to remain dynamic, with ongoing innovation and competition driving growth and development.

How is this Compensation Software Industry segmented?

The compensation software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Large enterprises
  SMEs


Deployment

  Cloud-based
  On-premises


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By End-user Insights

The large enterprises segment is estimated to witness significant growth during the forecast period.

In the dynamic business landscape, compensation software solutions have emerged as essential tools for managing intricate compensation strategies and policies. Large enterprises dominate the mark

Annual salary information including gross pay and overtime pay for all active, permanent employees of Montgomery County, MD paid in calendar year 2023. This dataset is a prime candidate for conducting analyses on salary disparities, the relationship between department/division and salary, and the distribution of salaries across gender and grade levels.

Statistical models can be applied to predict base salaries based on factors such as department, grade, and length of service. Machine learning techniques could also be employed to identify patterns and anomalies in the salary data, such as outliers or instances of significant inequity.

Some analysis to be performed with this dataset can include:

• Gender Pay Gap Analysis: An examination of salary differences between genders within similar roles, grades, and departments to identify any disparities that need to be addressed.
• Departmental Salary Analysis: Analyzing the distribution of salaries across different departments and divisions to understand how compensation varies within the organization.
• Impact of Overtime and Longevity Pay: Evaluating how overtime and longevity pay contribute to the overall compensation of employees and identifying trends or patterns in these payments. ​

Dive into Market Research Intellect's Earned Wage Access Software Market Report, valued at USD 3.5 billion in 2024, and forecast to reach USD 11.2 billion by 2033, growing at a CAGR of 15.5% from 2026 to 2033.

The Procure To Pay Software Market is Segmented by Component (Software / Platform, Implementation and Managed Services), Deployment (Cloud, On-Premises), End-User Enterprise (SMEs, Large Enterprise), End-User Industry (BFSI, Healthcare and Life-Sciences, Public Sector and Education, Retail and E-Commerce, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Pay Trend Survey results since 2007

1) Data Introduction • The Data Science Salaries 2023 Dataset is a global annual salary analysis dataset that summarizes a variety of information in a tabular format, including salary, career, employment type, job, remote work rate, and company location and size for data science jobs as of 2023.

2) Data Utilization (1) Data Science Salaries 2023 Dataset has characteristics that: • Each row contains 11 key characteristics, including year, career level, employment type, job name, annual salary (local currency and USD), employee country of residence, remote work rate, company location, and company size. • Data is organized to reflect different countries, jobs, careers, and work patterns to analyze pay and work environments in data science in three dimensions. (2) Data Science Salaries 2023 Dataset can be used to: • Data Science Salary Analysis and Comparison: Analyzing salary levels and distributions by job, career, country, and company size can be used to understand industry trends and market value. • Establishing Recruitment and Career Strategies: It can be applied to recruitment strategies, career development, global talent attraction, etc. by analyzing the correlation between various working conditions and salaries such as remote work rates, employment types, and company location.

Graph and download economic data for Average Hourly Earnings of All Employees, Total Private (CES0500000003) from Mar 2006 to Jul 2025 about average, earnings, hours, establishment survey, wages, private, employment, and USA.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Neenah. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Neenah, the median income for all workers aged 15 years and older, regardless of work hours, was $51,729 for males and $36,037 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 30% between the median incomes of males and females in Neenah. With women, regardless of work hours, earning 70 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Neenah.

- Full-time workers, aged 15 years and older: In Neenah, among full-time, year-round workers aged 15 years and older, males earned a median income of $65,337, while females earned $50,877, leading to a 22% gender pay gap among full-time workers. This illustrates that women earn 78 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Neenah, showcasing a consistent income pattern irrespective of employment status.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Neenah median household income by race. You can refer the same here

The global salary management system software market is projected to reach a value of USD 25.58 billion by 2033, exhibiting a CAGR of 9.8% during the forecast period (2023-2033). The growing adoption of cloud-based solutions, the increasing need for automated payroll processes, and the rising demand for employee self-service portals are some of the key factors driving the market growth. Additionally, the increasing adoption of mobile applications for salary management purposes is further fueling the market expansion. North America and Europe are the dominant regions in the salary management system software market, owing to the presence of a large number of established vendors and the early adoption of advanced technologies. The Asia Pacific region is expected to witness significant growth in the coming years due to the increasing adoption of cloud-based solutions and the growing number of SMEs in the region. Key players in the market include Dew CIS Solution Limited, Workday, Inc., Paycom Software, Inc., Ceridian, Ultimate Kronos Group, Payfactors, Salary.com, PayScale, SAP, Oracle, HRsoft, and Ascentis Corporation.