This dataset contains information about salaries of various roles in the data science field, collected from around the globe. It includes data on work year, experience level, job titles, and salaries, along with details on employment type, remote work ratio, and company size.

This dataset is suitable for analysis of salary trends across different job roles, locations, and experience levels, and can help uncover insights into the data science job market.

Columns and Definitions

• work_year: The year the salary data was collected (e.g., 2023, 2024).
• experience_level: The experience level of the employee.
  • EN: Entry-level/Junior
  • MI: Mid-level
  • SE: Senior-level
  • EX: Executive-level
• employment_type: The type of employment contract (e.g., FT - Full Time).
• job_title: The title of the job (e.g., Data Scientist, Machine Learning Engineer).
• salary: Salary in the specified currency.
• salary_currency: The currency of the salary (e.g., USD, EUR, GBP).
• salary_in_usd: The salary converted to USD for comparison.
• employee_residence: The primary country of residence of the employee.
• remote_ratio: The percentage of remote work (0: On-site, 50: Hybrid, 100: Fully Remote).
• company_location: The country where the company is located.
• company_size: Size of the company (S: Small, M: Medium, L: Large).

Usage of Data

This dataset can be used to:

• Analyze global salary trends in data science roles.
• Study the impact of remote work and company size on salaries.
• Compare salary distributions across different countries and experience levels.
• Train machine learning models to predict salaries based on job roles and other features.

Graph and download economic data for Employed full time: Wage and salary workers: Technical writers occupations: 16 years and over: Men (LEU0254593100A) from 2000 to 2024 about occupation, full-time, males, salaries, workers, 16 years +, wages, employment, and USA.

Graph and download economic data for Employed full time: Wage and salary workers: Healthcare practitioner and technical occupations: 16 years and over (LEU0254487000A) from 2000 to 2024 about healthcare, occupation, full-time, health, salaries, workers, 16 years +, wages, employment, and USA.

This Dataset containes the details of the AI, ML, Data Science Salary (2020- 2025). Salary data is in USD and recalculated at its average fx rate during the year for salaries entered in other currencies.

The data is processed and updated on a weekly basis so the rankings may change over time during the year.

Attribute Information

• 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: 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 of respective year) via statistical data from the BIS and central banks.
• 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/hybird 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)

Acknowledgements

https://aijobs.net/

Photo by Anastassia Anufrieva on Unsplash

Cite @inproceedings{kverne2025course, title={Course-Job Fit: Understanding the Contextual Relationship Between Computing Courses and Employment Opportunities}, author={Kverne, Christopher Lukas and Monteverdi, Federico and Polyzou, Agoritsa and Lisetti, Christine and Bhimani, Janki}, booktitle={2025 ASEE Annual Conference & Exposition}, year={2025} }

Data Collection This is a Data set collected from October 2024 - December 2024 with 100 000 tech jobs in the US from fields such as 1. Cyber Security (cs_jobs.xlsx) 2. Software Engineering (swe_jobs.xlsx) 3. IT (Information Technology) (it_jobs.xlsx) 4. Product Management (pm_jobs.xlsx) 5. Data Science (ds_jobs.xlsx)

all_jobs.xlsx combines all of these files and jobs

The jobs were collected with JobsPy (A job scraper) and fetched from sites like Indeed, ZipRecruiter, and Glassdoor.

Data Information The jobs have various features and all include a job description perfect for NLP tasks! Here are some notable features (although more exists): 1. Site (what website fetched it like indeed) 2. Location 3. Company 4. Title 5. Min Salary (Salary Range) 6. Max Salary (Salary Range) 7. Description (Job Description)

While we used this Dataset for comparing courses with jobs I encourage you to be as creative as possible. The Salary, Description, Title and Location have infinite possibilities and I'm excited to see what you'll use this data set for.

GitHub Repository Can be Found Here: https://github.com/Damrl-lab/Course-Job-Fit

In 2022, on average, women were offered *** percent less salary compared to men when they applied for the same job title at the same company in the technology industry.

Landing high-paying jobs at these top tech giants requires a fine blend of education, skills, and practical experience. You need to have a deep understanding of data and its analysis, something that can be massively boosted by applying for a degree at IU. The data science degrees we offer, ranging from a Bachelor's in Data Science to an MBA in Big Data Management, provide hands-on, applicable knowledge which these top tech leaders value

Networking, demonstrating creative problem-solving, ability to identify patterns, and showcasing a portfolio of practical projects can also go a long way in getting noticed. Remember, these companies are looking for innovative minds who can use data to drive their companies forward!

IBM, a veteran of the tech industry, has long since recognised the value of data and employs data scientists to keep them on the cutting edge of technology. The average salary of a data scientist at IBM is $155,869[3] per year, along with other incentives such as bonuses.

Google, requires data scientists to improve its user experience, advertising platform and search algorithms among other things. Data scientists at Google can expect to earn $135,287[2] annually, as per recent figures provided by Glassdoor [2023].

As one of the world's largest and most successful e-commerce corporations, Amazon has a high demand for data scientists to analyse and interpret the vast volume of data they generate. The average base salary for a Data Scientist in Amazon is $128,059[1] per year in the United States, with additional compensation like bonuses and benefits [2023].

Anonymous salary data for technology professionals across Australia and New Zealand in 2025, including base salary, equity, bonuses, and benefits information.

In a 2022 survey of current employees and jobseekers in the global technology industry, respondents identified different groups as being most negatively impacted by wage bias in their organizations. Approximately ** percent of respondents reported that women were the most affected group. In contrast, about **** percent and **** percent of respondents indicated that age and nationality, respectively, were the groups most negatively impacted by wage bias.

Dataset

This dataset was created by TommyD

Contents

United States - Employed full time: Wage and salary workers: Technical writers occupations: 16 years and over: Women was 32.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Technical writers occupations: 16 years and over: Women reached a record high of 38.00000 in January of 2001 and a record low of 22.00000 in January of 2006. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Technical writers occupations: 16 years and over: Women - last updated from the United States Federal Reserve on December of 2025.

Graph and download economic data for Consumer Price Index for All Urban Wage Earners and Clerical Workers: Information Technology, Hardware and Services in U.S. City Average (CWUR0000SEEE) from Dec 1988 to Sep 2025 about hardware, information technology, clerical workers, information, urban, wages, services, CPI, inflation, price index, indexes, price, and USA.

Graph and download economic data for Employed: Paid at prevailing federal minimum wage: Private wage and salary workers: Professional and technical services industries: 16 years and over (LEU0204853600A) from 2000 to 2024 about paid, minimum wage, professional, salaries, workers, 16 years +, federal, wages, services, private, employment, industry, and USA.

As of May 2023, the average wage for employees in high-tech manufacturing of computers, electronic and optical products in Israel was ****** Israeli shekels (roughly ***** U.S. dollars). This was a significant decrease compared to the previous month, April of the same year. The average wage of employees in this high-tech field in the country fluctuated during the period under review, overall remaining stable at an average of roughly ****** Israeli shekels (just over ***** U.S. dollars).

United States CPI W: EC: Comm: IP: Information Technology (IT) data was reported at 8.093 Dec1988=100 in Jun 2018. This records an increase from the previous number of 8.039 Dec1988=100 for May 2018. United States CPI W: EC: Comm: IP: Information Technology (IT) data is updated monthly, averaging 16.200 Dec1988=100 from Dec 1988 (Median) to Jun 2018, with 355 observations. The data reached an all-time high of 100.000 Dec1988=100 in Dec 1988 and a record low of 7.992 Dec1988=100 in Apr 2018. United States CPI W: EC: Comm: IP: Information Technology (IT) data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I012: Consumer Price Index: Urban Wage and Clerical Workers.

China Average Wage: Information Transmission, Software and Information Technology Service data was reported at 130,366.000 RMB in 2017. This records an increase from the previous number of 120,864.000 RMB for 2016. China Average Wage: Information Transmission, Software and Information Technology Service data is updated yearly, averaging 112,119.000 RMB from Dec 2013 (Median) to 2017, with 5 observations. The data reached an all-time high of 130,366.000 RMB in 2017 and a record low of 93,044.000 RMB in 2013. China Average Wage: Information Transmission, Software and Information Technology Service data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Labour Market – Table CN.GC: Average Wage: by Industry.

In the United States, the median income in 2023 was at 112,800 U.S. dollars for Asian households. This is a large increase from 2002 when the median income for Asian households was 84,770 U.S. dollars (in 2023 U.S. dollars).

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for big data analytics and information technology in the U.S.

Global IT Jobs Analysis

A Detailed Investigation of Salary, Location, and Job Requirements

By [source]

About this dataset

This dataset contains valuable insights into current job opportunities in the information technology (IT) sector all around the world. It offers an overview of available jobs and relevant data such as company, location, salary and links to further information. With this insight, one has the chance to better understand what it takes to land a remote or data-science job in today's global market. The ever increasing demand for IT workforce puts technical skills at a premium, so understanding exactly what employers are searching for can give potential employees an edge in catching the eye of these businesses! Digging through this dataset can provide details on current trends in terms of salary expectations and geographical locations where these roles are most popular. Beyond that, get an idea about which abilities seem most valuable when it comes to remote or data-science positions. Use this arsenal of knowledge to take your career goals into your own hands now!

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How to use the dataset

This dataset provides an opportunity to explore the remote and data-science job opportunities around the world. Using this dataset, you can analyze trends in job requirements, salary packages offered, location of available jobs and more. With the knowledge gained from this data set, individuals and companies can make more informed decisions about pursuing a certain path in their career or hiring for their business.

The dataset includes columns with important information such as Job title, Company offering the job, Location of the position , Salary offered for that position and a Link to its respective posting. Using these columns you can analyze various factors regarding global IT Jobs availability over different locations in alignment with salary offered for positions and any specific skill sets sought out by companies .

To get executable insights from this data set users should first load it into their respective computing environment (Python or R). After loading it in your environment users should start off by exploring Groupby statements along factors like Companies offering jobs ,Salary offered ,Location etc. followed by descriptive statistics like mean & median of Salary Levels per country/region etc. After getting basic insight about summary statistics for various factors belonging all together within “Job” range user could move forward to look over individual cases (specific skill sets) after which they could filter out & generate valueable insights needed .

With our comprehensive understanding of global supply & demand rates individuals/corporations could always use these datasets to help them keep track on talent acquisition landscape when they hire globally or relocating teams as companies who need such information would greatly benefit from versatile tools like this one that offer valuable actionsable insights on an ongoing basis depending upon dayers choosing!

Research Ideas

• Identifying the most in-demand skills and employment requirements for remote data science and IT jobs, across different countries and regions.
• Developing a prediction model to forecast future salary expectations for data science professionals based on location, company, job type, etc.
• Building an interactive dashboard with visualizations showing differences in job requirements (by level of experience or education), salary comparison across geographies as well as potential career paths one can pursue within the IT or Data Science fields

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: Job_listing.csv | Column name | Description | |:--------------|:---------------------------------------------------| | Job | The title of the job listing. (String) | | Company | The name of the company offering the job. (String) | | Location | The geographic location of the job. (String) | | Salary | The salary offere...

China Real Wage Index: Urban Non-private: Information Transmission, Software and Information Technology Service data was reported at 107.241 Prev Year=100 in 2022. This records a decrease from the previous number of 112.373 Prev Year=100 for 2021. China Real Wage Index: Urban Non-private: Information Transmission, Software and Information Technology Service data is updated yearly, averaging 107.464 Prev Year=100 from Dec 2004 (Median) to 2022, with 18 observations. The data reached an all-time high of 114.168 Prev Year=100 in 2005 and a record low of 104.520 Prev Year=100 in 2011. China Real Wage Index: Urban Non-private: Information Transmission, Software and Information Technology Service data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Labour Market – Table CN.GC: Real Wage Index.