It’s hard to get real-world information about what jobs pay, ALISON GREEN published a survey in 2021 on AskAManager.org, a US-centric-ish but does allow for a range of country inputs. The survey is designed to examine payment of different industries based on experience years, field experience years among other variables such as gender, race and education level.

The dataset is “live” and constantly growing, our dataset was downloaded in 23/2/2023.

Data Dictionary

The original dataset includes the following fields: * Age: How old are you? * Industry: What industry do you work in? * Job title: What is your job title? * Extra_job_title: If your job title needs additional context, please clarify here * Annual_salary: "What is your annual salary? If you are part-time or hourly, please enter an annualized equivalent -- what you would earn if you worked the job 40 hours a week, 52 weeks a year.)
* Annual_bonus: How much additional monetary compensation do you get, if any (for example, bonuses or overtime in an average year) only include monetary compensation here, not the value of benefits. * Currency: Please indicate your salary currency. * Other_currency: 'If "Other," please indicate the currency here. * Extra_income_info: "If your income needs additional context, please provide it here. * Work_country: "What country do you work in? * Work_state_US: "If you're in the U.S., what state do you work in? * Work_city: "What city do you work in? * Overall_experience_years: "How many years of professional work experience do you have overall? * Field_experience_years: "How many years of professional work experience do you have in your field?" * Education_level: "What is your highest level of education completed? * Gender: "What is your gender? * Race:"What is your race? (Choose all that apply.)

This biennial survey provides information on wages and salaries for full- and part-time employees by occupation, region, and industry. The survey helps Albertans make career and education choices and helps organizations determine pay scales.

Popular US workplace blog AskAManager (askamanager.org) sponsors an annual salary survey of blog readers. The 2023 survey collected data about industry, job function, title, annual salary, additional compensation, race, gender, remote/on-site requirements, education, location, and years' experience.

The dataset here features responses collected between April 11 and 28, 2023, and has some 16,000 responses. This version of the data set has employed several feature engineering techniques to group and cleanse data, convert the currency to USD values as of April 1, 2023, and add clarity to location data. In particular, US respondents were paired when possible with a metropolitan area.

Average weekly earnings at industry level including manufacturing, construction and energy, Great Britain, monthly, non-seasonally adjusted. Monthly Wages and Salaries Survey.

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.

The National Compensation Survey (NCS) program produces information on wages by occupation for many metropolitan areas.The Modeled Wage Estimates (MWE) 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. The modeled wage estimates are produced using a statistical procedure that combines survey data collected by the National Compensation Survey (NCS) and the Occupational Employment Statistics (OES) programs. Borrowing from the strengths of the NCS, information on job characteristics and work levels, and from the OES, the occupational and geographic detail, the modeled wage estimates provide more detail on occupational average hourly wages than either program is able to provide separately. Wage rates for different work levels within occupation groups also are published. Data are available for private industry, State and local governments, full-time workers, part-time workers, and other workforce characteristics.

The statistic gives the results of the annual salary survey among logistics and supply chain professionals, asking respondents about their annual salaries including bonuses and other compensations in 2016 and 2017, and broken down by job function. In that period, the average salary for a supply chain management employee amounted to about ******* U.S. dollars, down from ******* U.S. dollars in the previous year.

Comprehensive salary benchmarking dataset covering compensation data across major technology companies, job families, locations, and experience levels. Includes base salary, total compensation, equity, and bonus information.

The statistic displays the results of the annual salary survey among logistics and supply chain professionals, asking respondents about their annual salaries including bonuses and other compensations from 2018 to 2020, broken down by work experience in the field. During the 2020 survey, the average salary for an employee with over 30 years of experience in the logistics and supply chain management industries amounted to about 136,195 U.S. dollars, down from 144,530 U.S. dollars in the previous year.

Context

In the rapidly evolving field of data science, understanding the trends and patterns in salaries is crucial for professionals and organizations alike. This dataset aims to shed light on the landscape of Data Science Salaries from 2020 to 2024. By analyzing salary data over this period, data enthusiasts, researchers, and industry professionals can gain valuable insights into salary trends, regional variations, and potential factors influencing compensation within the data science community.

Content

The dataset encompasses a comprehensive collection of data science salary information, covering a span of five years from 2020 to 2024. The data includes various aspects related to salaries, providing a multifaceted view of compensation in the field.

Dataset Structure

This dataset (data_science_salaries) covering from 2020 up to 2024 includes the following columns:

Acknowledgment

The primary dataset was retrieved from the ai-jobs.net. I sincerely thank the team for providing the core data used in this dataset.

© Image credit: Freepik

Graph and download economic data for Employed full time: Wage and salary workers: Industrial and refractory machinery mechanics occupations: 16 years and over (LEU0254511300A) from 2000 to 2024 about mechanics, occupation, full-time, machinery, salaries, workers, 16 years +, wages, employment, industry, and USA.

Graph and download economic data for Unemployment Level - Information Industry, Private Wage and Salary Workers (LNU03032237) from Jan 2000 to Sep 2025 about information, salaries, workers, private industries, 16 years +, wages, household survey, private, unemployment, industry, and USA.

Explore a detailed dataset of employees' compensation by type and economic activity in Saudi Arabia. This dataset covers a wide range of industries, from manufacturing to healthcare, providing valuable insights for economic analysis and decision-making.

Other manufacturing, Remediation activities and other waste management services, Industry of paper and its products, Health and social work, Extraction of crude petroleum and natural gas, Social work activities without accommodation, Manufacture of food prod. and beverages, Manufacture of textiles, Financial intermediation, Motion picture, video and tv programme production, sound recording, Scientific research and development, Hotels and restaurants, Other personal service activities, Retail trade, except of motor vehicles and motorcycles, Information service activities, Manufacturing of apparel, preparing and tanning fur, Food and beverage service activities, Manufacture of food products, Manufacture of leather and related products, Repair and installation of machinery and equipment, Programming and broadcasting activities, Other mining and quarrying, Education, Manufacture of office, accounting and computing machinery, Creative, arts and entertainment activities, Insurance and pension funding, except compulsory social security, Construction, Sports activities and amusement and recreation activities, Printing and reproduction of recorded media, Travel agency, tour operator...

Saudi Arabia Follow data.kapsarc.org for timely data to advance energy economics research..Data from the Annual Economic Establishment Survey.Do not include establishments operating in the governmental and external sectors. Including establishments operating in the private and public sector and not for profit.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by North American Industry Classification System (NAICS), type of work, gender, and age group.

This dataset consists of an anonymous survey on Jobs and salaries related to data science positions including details like work life balance, happiness on both quality of work and salary their preferred programming language and the industry they are working for

Graph and download economic data for Employment Level - Agriculture and Related Industries, Wage and Salary Workers (LNS12032184) from Jan 1948 to Sep 2025 about agriculture, salaries, workers, 16 years +, wages, household survey, employment, industry, and USA.

Graph and download economic data for Employment Level - Nonagriculture, Private Wage and Salary Workers, Other Industries (LNS12035078) from Jan 1999 to Sep 2025 about nonagriculture, salaries, workers, 16 years +, wages, household survey, private, employment, industry, and USA.

Public authorities are required by Section 2800 of Public Authorities Law to submit annual reports to the Authorities Budget Office that includes salary and compensation data. The dataset consists of salary data by employee reported by Industrial Development Agencies that covers 8 fiscal years, which includes fiscal years ending in the most recently completed calendar year.

Philippines Employment: Wage & Salary Workers: With Pay in Own Family Business data was reported at 211.000 Person th in Feb 2025. This records an increase from the previous number of 106.000 Person th for Jan 2025. Philippines Employment: Wage & Salary Workers: With Pay in Own Family Business data is updated monthly, averaging 160.500 Person th from Jan 2021 (Median) to Feb 2025, with 50 observations. The data reached an all-time high of 312.000 Person th in Nov 2022 and a record low of 76.000 Person th in Dec 2024. Philippines Employment: Wage & Salary Workers: With Pay in Own Family Business data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.G025: Labour Force Survey: Employment: by Industry, Occupation and Class.

According to the 2022/23 salary survey report on supply chain industry annual salaries in Australia, logistics directors earned between *** and *** thousand Australian dollars per year. By comparison, a transport planner in supply chain logistics earned a maximum salary of around 100 thousand Australian dollars.