Dataset Card for Data Science Job Salaries

  Dataset Summary





  Content

Column Description

work_year The year the salary was paid.

experience_level The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director

employment_type The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance

job_title… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/data-science-job-salaries.

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

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.

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 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

The average annual salary of a Data Architect in India was estimated to be over *********** Indian rupees per annum, the highest among other jobs in the Data Science sector in India. It was followed by data Scientist and Database Developer roles.

🧠 About the Data

This dataset explores how remote work opportunities intersect with salaries, experience, and employment types across industries. It contains clean, structured records of 500 hypothetical employees in remote or hybrid job roles, suitable for salary modeling, HR analytics, or industry-based salary insights.

📌 Column Descriptions

📈 Potential Use Cases

• Predictive modeling for salary based on role, experience, and location
• Salary benchmarking per industry or employment type
• Visualizing remote vs onsite salary disparities
• Market research for HR and hiring trends
• Exploratory analysis on global employment models

In 2024, people working in IT management in the United States, earned an average annual salary worth around 168 thousand U.S. dollars. Software developers and project managers all reported being paid on average over 120 thousand U.S. dollars. Despite nearly all categories saw a year-on-year increase in annual compensation, IT support and help desk technicians saw a decrease compared to the previous year

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.

About this Dataset

This dataset combines automation probability data with a breakdown of the number of jobs and salary in each occupation by state within the USA. Automation probability was acquired from the work of Carl Benedikt Freyand Michael A. Osborne; State employment data is from the Bureau of Labor Statistics. Note that for simplicity of analysis, all jobs where data was not available or there were less than 10 employees were marked as zero.

How to Cite this Dataset

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

Salary Data

@misc{u.s. bureau of labor statistics, title={Occupational Employment Statistics}, url={https://www.bls.gov/oes/current/oes_nat.htm}, journal={U.S. BUREAU OF LABOR STATISTICS}}

Automation Data

@article{frey_osborne_2017, title={The future of employment: How susceptible are jobs to computerisation?}, volume={114}, DOI={10.1016/j.techfore.2016.08.019}, journal={Technological Forecasting and Social Change}, author={Frey, Carl Benedikt and Osborne, Michael A.}, year={2017}, pages={254–280}}

License

License was not specified at the source.

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Graph and download economic data for Employed full time: Wage and salary workers: Job printers occupations: 16 years and over: Women (LEU0254729900A) from 2000 to 2010 about occupation, jobs, females, full-time, salaries, workers, 16 years +, wages, employment, and USA.

Current Employment by Industry (CES) data reflect jobs by "place of work." It does not include the self-employed, unpaid family workers, and private household employees. Jobs located in the county or the metropolitan area that pay wages and salaries are counted although workers may live outside the area. Jobs are counted regardless of the number of hours worked. Individuals who hold more than one job (i.e. multiple job holders) may be counted more than once. The employment figure is an estimate of the number of jobs in the area (regardless of the place of residence of the workers) rather than a count of jobs held by the residents of the area.

This occupational wage dataset is based on Occupational Employment Statistics (OES) survey that captures 52,000 businesses. This particular dataset is on healthcare practitioners, technical occupation and healthcare support occupation. The other data set in this series include healthcare support occupations.

Wages in the United States increased 4.72 percent in May 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.

What's inside dataset

work_year

The year during which the salary was paid. There are two types of work year values: 2020 Year with a definitive amount from the past 2021e Year with an estimated amount (e.g. current year)

experience_level

The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director

employment_type

The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance

job_title

The role worked in during the year. salary The total gross salary amount paid.

salary_currency

The currency of the salary paid as an ISO 4217 currency code.

salary_in_usd

The salary in USD (FX rate divided by avg. USD rate for the respective year via fxdata.foorilla.com).

employee_residence

Employee's primary country of residence in during the work year as an ISO 3166 country code.

remote_ratio

The overall amount of work done remotely, possible values are as follows: 0 No remote work (less than 20%) 50 Partially remote 100 Fully remote (more than 80%)

company_location

The country of the employer's main office or contracting branch as an ISO 3166 country code.

company_size

The average number of people that worked for the company during the year: S less than 50 employees (small) M 50 to 250 employees (medium) L more than 250 employees (large)

Dataset Source - ai-jobs.net Salaries

Graph and download economic data for Employed full time: Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over: Men (LEU0257856300A) from 2011 to 2024 about occupation, jobs, full-time, males, compensation, benefits, salaries, workers, 16 years +, wages, employment, and USA.

Job Postings in Europe

Exploring Salaries, Job Types and Locations

By [source]

About this dataset

The eMedCareers Europe dataset contains more than 30,000 job postings from the top companies across Europe. This comprehensive data set includes detailed information about each job posting such as the associated job category, company name, location, job title and description, as well as types of jobs and salary offered. With this data set, researchers can gain insights into the current trends in salaries and job types in various parts of Europe. Moreover, it provides unique insights into which companies are actively hiring for specific positions and wage levels to assist businesses in forming competitive salaries structures. With this dataset at your fingertips you can start uncovering intriguing patterns in European employment and pay scales - providing deep understandings of the current hiring climate across multiple countries within the region

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How to use the dataset

This dataset contains information on job postings from top companies in Europe. It can be used to analyze job types, salaries and locations of advertised positions. The data fields include: - Category: This field specifies the type or category of the position being advertised, such as Engineering, Marketing or Accounting. - Company Name: This field identifies the company advertising the position. - Job Description: This field provides a short description about what will be expected from an individual in this role. - Job Title: This field displays the title of the role that is being offered. - Job Type: This field specifies full-time, part-time contract work etc which would either be available for direct hire or freelance gigs. - Location: This field denotes where these positions are located in Europe and who could apply for it based on their location/residence. - Salary Offered :This filed provides gross annual salary or pay range that is being offered by employer to employee who takes up this job title and other compensation benefits as part per contract terms and conditions set while signing up for specific roles in company/organization

Using this dataset you can easily analyze all these different aspects related to job openings in Europe available at eMedCareers portal like salary statistics for different industries/categories, job types – full-time vs freelance/contract; location wise jobs availability etc making more informed decision when looking out into market looking out new career opportunities with prospective employers based upon your skillset

Research Ideas

• Analyzing the correlation between salary offered and job type (full-time, part-time, contract) to identify salary trends across different job types in Europe.
• Using the job category and location data to create a geographical analysis of demand for certain roles and skillsets in Europe.
• Tracking changes in the average salaries over time by visualizing posting date vs salary_offered data points

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: emed_careers_eu.csv | Column name | Description | |:--------------------|:-------------------------------------------------------| | category | The job category of the posting. (String) | | company_name | The name of the company posting the job. (String) | | job_description | A description of the job. (String) | | job_title | The title of the job. (String) | | job_type | The type of job (full-time, part-time, etc.). (String) | | location | The location of the job. (String) | | post_date | The date the job was posted. (Date) | | salary_offered | The salary offered for the job. (Integer) |

Acknowledgements

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

Detailed labour market outcomes by educational characteristics, including detailed occupation, hours and weeks worked and employment income.

Real-time API access to rich Job Postings Data with 200M+ job postings & Salary Data sourced from Google for Jobs - global aggregate of LinkedIn, Indeed, Glassdoor, ZipRecruiter, and all public job sites across the web.