This Dataset indicates average salary by position title and grade for full-time regular employees. Data excludes elected, appointed, non-merit and temporary employees. Underfilled positions are also excluded from the dataset. Update Frequency : Annually

As of 2023, the median wage for employees in healthcare support occupations was about 36,140 U.S. dollars. The occupational group with the highest annual median wage was management occupations. Mean wages for the same occupational groups can be accessed here.

This dataset provides a comprehensive collection of salary information from various industries and regions across the globe. Sourced from reputable employment websites and surveys, it includes details on job titles, salaries, job sectors, geographic locations, and more. Analyze this data to gain insights into job market trends, compare compensation across different professions, and make informed decisions about your career or hiring strategies. The dataset is cleaned and preprocessed for ease of analysis and is available under an open license for research and data analysis purposes.

Education Level: 0 : High School 1 : Bachelor Degree 2 : Master Degree 3 : Phd

Currency : US Dollar

Senior : It shows that is this employee has a senior position or no.(Binary)

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.

Average Wage per Job in Maryland and Its Jurisdictions (in Constant 2024 Dollars), 2014–2024, based on data from the Quarterly Census of Employment and Wages (QCEW), which includes all workers covered under the State Unemployment Insurance (UI) program and the Unemployment Compensation for Federal Employees (UCFE). The 2024 annual average wage figures are preliminary. Hand-calculated total may differ from the published total due to data suppression and privacy protection. Source: The U.S. Census Bureau of Labor Statistics, Quarterly Census Employment and Wages (QCEW), 2014-2024, June 2025.

Are you looking for a new career opportunity? Do you want to explore the job market and see what skills and qualifications are in demand? Or are you curious about the characteristics and performance of different companies?

If you answered yes to any of these questions, then this dataset is for you! This dataset contains two files: one with over 100,000 job postings, and another with information about more than 30,000 companies from different industries and locations. All the info is from US market, and comes from the website Indeed.

Check the extraction and cleaning process on my GitHub repository:

jobs-data-cleaning

The jobs file includes the following fields:

• Job title
• Company name
• Job Profile
• Remote/Hybrid
• Location
• Salary
• Job requirements

The companies file includes the following fields:

• Company name
• Industry
• Location
• Website
• Number of employees
• Revenue

With this dataset, you can:

• Analyze the job market trends and identify the most popular and lucrative jobs, skills, and qualifications.
• Compare and contrast different companies and see how they perform in terms of revenue, profit, growth, and employee satisfaction.
• Create visualizations and dashboards to showcase your findings and insights.
• Build machine learning models to predict salary, job satisfaction, company rating, or any other outcome of interest.

This dataset is a valuable resource for anyone interested in career development, business analysis, data science, or machine learning. Download it now and start exploring!

Gross weekly and hourly earnings by level of occupation, UK, quarterly, not seasonally adjusted. Labour Force Survey. These are official statistics in development.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by National Occupational Classification (NOC), type of work, gender, and age group.

This dataset provides comprehensive information on the average income and rent in various states across the United States for the year 2022. It aims to offer insights into state-level economic trends and housing market dynamics.

Column Descriptions:

Region: Name of the state within the United States.

Average_Rent: Description: Average monthly rent for residential properties in each state, reflecting prevailing rental costs.

Average_Income: Average per capita income within each state, representing the average earnings of individuals residing in the state over the year.

Asian households measured the highest median household income among racial and ethnic groups in the United States. In 2024, Asian household incomes reached a median of 121,700 U.S. dollars. On the other hand, Black households had the lowest median income of 56,020 U.S. dollars. Overall, median household incomes in the United States stood at 83,730 U.S. dollars that year.Asian and Caucasian (white not Hispanic) households had relatively high median incomes, while the median income of Hispanic, African American, American Indian, and Alaskan Native households all came in lower than the national median. A number of related statistics illustrate further the current state of racial inequality in the United States. Unemployment is highest among Black or African American individuals in the U.S. nearing nine percent unemployed, according to the Bureau of Labor Statistics in 2024. Hispanic individuals (of any race) were most likely to go without health insurance as of 2024.

Economically Active Population Survey: Average wages of the main job by period, type of working day, occupation and decile. Annual. National.

Explore the dataset on average salaries in the private sector by main profession, nationality, and gender in Saudi Arabia. Gain insights into industrial and chemical processes, food industries, total labor force, and more.

Industrial and chemical processes and food industries, Non-Saudis, Total labour force, Agricultural and animal husbandry Poultry and fishing, Services jobs, Auxiliary basic engineering jobs, Scientific, technical and human technicians, Clerical jobs, Saudis, Male, Administrative and business directors, Other, Sales jobs, Scientific, technical and human specialists, Female, Profession, Gender , Saudi, Non Saudi, SAMA Annual

Saudi Arabia Follow data.kapsarc.org for timely data to advance energy economics research..

Graph and download economic data for 12-Month Moving Average of Unweighted Median Hourly Wage Growth: Job Switcher (FRBATLWGT12MMUMHWGJSW) from Dec 1997 to Aug 2025 about growth, moving average, 1-year, jobs, average, wages, median, and USA.

Graph and download economic data for 3-Month Moving Average of Unweighted Median Hourly Wage Growth: Job Movement: Job Stayer (FRBATLWGT3MMAUMHWGJMJST) from Mar 1997 to Aug 2025 about growth, moving average, jobs, 3-month, average, wages, median, and USA.

About Dataset

Context

Analytics refers to the methodical examination and calculation of data or statistics. Its purpose is to uncover, interpret, and convey meaningful patterns found within the data. Additionally, analytics involves utilizing these data patterns to make informed decisions. It proves valuable in domains abundant with recorded information, employing a combination of statistics, computer programming, and operations research to measure performance.

Businesses can leverage analytics to describe, predict, and enhance their overall performance. Various branches of analytics encompass predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, graph analytics, credit risk analysis, and fraud analytics. Due to the extensive computational requirements involved (particularly with big data), analytics algorithms and software utilize state-of-the-art methods from computer science, statistics, and mathematics.

Data Dictionary

Content

This Dataset contains information of 22700+ Software Professionals with different features like their Salaries (₹), Name of the Company, Company Rating, Number of times Salaries Reported, and Location of the Company.

Extra Features Added: 1. Employment Status 2. Job Roles

Acknowledgements

This Dataset is created from https://www.glassdoor.co.in/. If you want to learn more, you can visit the Website.

Roles Included:

Android Developer Android Developer - Intern Android Developer - Contractor Android Developer Contractor Senior Android Developer Android Software Engineer Android Engineer Android Applications Developer - Intern Android Applications Developer Android App Developer - Intern Senior Android Developer and Team Lead Android Tech Lead Product Engineer (Android) Software Engineer - Android Android Software Developer Android Software Developer - Intern Senior Android Developer Contractor Junior Android Developer - Intern Junior Android Developer Android Applications Developer - Contractor Android App Developer Lead Android Developer Android Engineer - Intern Sr. Android Developer Senior Android Engineer Senior Software Engineer - Android Android - Intern Android Android & Flutter Developer - Intern Associate Android Developer Senior Android Applications Developer Android Developer Trainee Sr Android developer Android Trainee Android Trainee - Intern Trainee Android Developer Android Lead Android Lead Developer Android Development - Intern Android Development Android Team Lead Senior, Android Developer Lead Android Engineer Tech Lead- Android Applications Developer Senior Android Software Developer Full Stack Android Developer Android Framework Developer Android Architect Android & Flutter Developer Senior Software Engineer, Android Android App Development Sr Android Engineer Android Team Leader Android Technical Lead SDE2(Android) Web Developer/Android Developer - Intern Android Applications Develpoers Android Platform Developer - Intern Android Test Engineer Senior Engineer - Android Android Framework Engineer Game Developer ( Android, Windows) Android Testing Senior Software Engineer (Android/Mobility) Ace - Android Development Software Developer (Android) - Intern Android Mobile Developer Android and Flutt...

Graph and download economic data for 3-Month Moving Average of Unweighted Median Hourly Wage Growth: Job Movement: Job Switcher (FRBATLWGT3MMAUMHWGJMJSW) from Mar 1997 to Aug 2025 about growth, moving average, jobs, 3-month, average, wages, median, and USA.

This dataset provides comprehensive information on salaries for various job roles across different countries. It includes details such as job titles, categories, local currency salaries, currency exchange rates, and converted salaries in US dollars. The data is sourced from worldsalaries.com and is subject to the website's terms of use and license.

Pointers

• The dataset covers a wide range of countries and job roles, allowing for cross-country and cross-industry comparisons.
• Local currency salaries are provided, along with the corresponding exchange rates and converted salaries in US dollars.
• Job roles are categorized into different groups, making it easier to analyze salaries within specific industries or sectors.
• The dataset can be used for various purposes, such as salary benchmarking, cost of living analysis, and labor market research.

How to Use

The dataset is provided in a tabular format, with each row representing a unique combination of country, job title, category, and salary information. Users can filter, sort, and analyze the data based on their specific requirements. It is recommended to handle the dataset using spreadsheet software or data analysis tools for efficient manipulation and analysis.

Data Source

The dataset is sourced from https://worldsalaries.com, a website dedicated to providing comprehensive salary information from around the world.

License and Terms of Use 📄

The license and terms of use for this dataset are as per the worldsalaries.com website's terms of use. Please respect their terms and adhere to them.

Column Descriptions

1. Country: The name of the country for which the salary information is provided.
2. Job Title: The specific job title or role for which the salary is listed.
3. Category: The broader category or industry to which the job title belongs.
4. Salary (Local Currency): The salary amount in the local currency of the respective country.
5. Currency: The currency code or symbol representing the local currency of the country.
6. Exchange Rate: The exchange rate value used to convert the local currency salary to US dollars.
7. Salary (Current USD): The salary amount converted to US dollars using the provided exchange rate.

Example

• Country: Afghanistan
• Job Title: Account Examiner
• Category: Accounting and Finance
• Salary (Local Currency): 501400 AFN (Afghan Afghani)
• Currency: AFN
• Exchange Rate: 71.31
• Salary (Current USD): 7037.74 (Converted salary in US dollars)

This dataset provides insights into data science job salaries from 2020 to 2025, including information on experience levels, employment types, job titles, and company characteristics. It serves as a valuable resource for understanding salary trends and factors influencing compensation in the data science field.

Features:

Usage:

• This dataset can be utilized for analyzing salary trends and variations in data science jobs over time and across different demographics.
• It can aid in benchmarking salaries, understanding the impact of factors such as experience level and company size on compensation, and informing career decisions in the data science field.

License:

This data set is made available by ai-jobs.net Salaries. Thank you for aggregating this information!

Context:

The Occupational Employment Statistics (OES) and National Compensation Survey (NCS) programs have produced estimates by borrowing from the strength and breadth of each survey to provide more details on occupational wages than either program provides individually. Modeled wage estimates provide annual estimates of average hourly wages for occupations by selected job characteristics and within geographical location. The job characteristics include bargaining status (union and nonunion), part- and full-time work status, incentive- and time-based pay, and work levels by occupation.

Direct estimates are based on survey responses only from the particular geographic area to which the estimate refers. In contrast, modeled wage estimates use survey responses from larger areas to fill in information for smaller areas where the sample size is not sufficient to produce direct estimates. Modeled wage estimates require the assumption that the patterns to responses in the larger area hold in the smaller area.

The sample size for the NCS is not large enough to produce direct estimates by area, occupation, and job characteristic for all of the areas for which the OES publishes estimates by area and occupation. The NCS sample consists of 6 private industry panels with approximately 3,300 establishments sampled per panel, and 1,600 sampled state and local government units. The OES full six-panel sample consists of nearly 1.2 million establishments.

The sample establishments are classified in industry categories based on the North American Industry Classification System (NAICS). Within an establishment, specific job categories are selected to represent broader occupational definitions. Jobs are classified according to the Standard Occupational Classification (SOC) system.

Content:

Summary: Average hourly wage estimates for civilian workers in occupations by job characteristic and work levels. These data are available at the national, state, metropolitan, and nonmetropolitan area levels.

Frequency of Observations: Data are available on an annual basis, typically in May.

Data Characteristics: All hourly wages are published to the nearest cent.

Acknowledgements:

This dataset was taken directly from the Bureau of Labor Statistics and converted to CSV format.

Inspiration:

This dataset contains the estimated wages of civilian workers in the United States. Wage changes in certain industries may be indicators for growth or decline. Which industries have had the greatest increases in wages? Combine this dataset with the Bureau of Labor Statistics Consumer Price Index dataset and find out what kinds of jobs you would need to afford your snacks and instant coffee!

The wages on the Job Bank website are specific to an occupation and provide information on the earnings of workers at the regional level. Wages for most occupations are also provided at the national and provincial level. In Canada, all jobs are associated with one specific occupational grouping which is determined by the National Occupational Classification. For most occupations, a minimum, median and maximum wage estimates are displayed. They are update annually. If you have comments or questions regarding the wage information, please contact the Labour Market Information Division at: NC-LMI-IMT-GD@hrsdc-rhdcc.gc.ca