In 2024, over 40 percent of developers globally reported wanting a higher salary or better career opportunities as their top reasons to switch jobs. Interestingly, escaping boredom or finding new challenges were the third-leading reason developers reported switching jobs, highlighting the importance of engaging work in tech roles.

####About Dataset

This dataset was retrieved from the page https://ai-jobs.net/salaries/download/

This site collects salary information anonymously from professionals all over the world in the AI, ML, Data Science space and makes it publicly available for anyone to use, share and play around with.

The primary goal is to have data that can provide better guidance in regards to what's being paid globally. So newbies, experienced pros, hiring managers, recruiters and also startup founders or people wanting to make a career switch can make better informed decisions.

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 data from 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/hybrid 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)

In 2019, about 75.8 percent of Generation Zs in Thailand expected to get a higher salary when they switched to a new job. Other expectations from Gen Zs while looking for a new job were finding a job that matches their skills and interests and that the job lets them advance in their career.

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

In 2025, there were estimated to be approximately 3.6 billion people employed worldwide, compared to 2.23 billion people in 1991 - an increase of around 1.4 billion people. There was a noticeable fall in global employment between 2019 and 2020, when the number of employed people fell from due to the sudden economic shock caused by the COVID-19 pandemic. Formal vs. Informal employment globally Worldwide, there is a large gap between the informally and formally employed. Most informally employed workers reside in the Global South, especially Africa and Southeast Asia. Moreover, men are slightly more likely to be informally employed than women. The majority of informal work, nearly 90 percent, is within the agricultural sector, with domestic work and construction following behind. Women’s employment As the number of employees has risen globally, so has the number of employed women. Overall, care roles such as nursing and midwifery have the highest shares of female employees globally. Moreover, while the gender pay gap has shrunk over time, it still exists. As of 2024, the uncontrolled gender pay gap was 0.83, meaning women made, on average, 83 cents per every dollar earned by men.

Graph and download economic data for Employed full time: Median usual weekly real earnings: Wage and salary workers: 16 years and over (LES1252881600Q) from Q1 1979 to Q1 2025 about full-time, salaries, workers, earnings, 16 years +, wages, median, real, employment, and USA.

Whether we’re looking for a new job as a new year’s resolution, switching career paths, or looking to progress into a more advanced role, we Brits carry out a vast array of online searches relating to new jobs every single day. But which jobs are most in-demand in 2024?

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

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.

Graph and download economic data for Employment Cost Index: Total compensation for Private industry workers in Transportation and material moving (CIS2010000520000I) from Q4 2005 to Q1 2025 about ECI, materials, compensation, workers, transportation, private industries, private, industry, and USA.

The thing that keeps open source professionals from changing jobs, according to a 2022 global survey of open source professionals, is a higher salary, with over half of respondents indicating as such. More training opportunities and flexible work schedules are also important to open source professionals.

The labor market exhibits persistent occupational segregation by gender, with women and men performing distinct job tasks within their occupations. Prior research suggests that non-routine job tasks generally lead to higher wages, especially in digitally advancing contexts. However, these findings are largely based on cross-sectional data and neglect gender as a relevant dimension of inequality. We analyze three-wave panel data over nine years from the German National Educational Panel Study to explore the relationship between changes in non-routine job tasks and wages by gender. Given the constrained wage-setting opportunities within German firms, we further examine whether the association between task changes and wages differs for employees with and without job changes, both within and across occupational segments. Our fixed-effect regression analyses reveal gender-specific associations between changes in non-routine job tasks and wage increases. Men benefit from performing more complex and autonomous tasks, with additional gains when an inter-segmental job change accompanies the increase in complex job tasks. Conversely, women do not see wage benefits from enhancements in either complex or autonomous job tasks. These findings underscore the gendered patterns of wage increases associated with advancements in non-routine job tasks, with men profiting intra-individually from shifts towards more non-routine job tasks.

Number of active employees, aggregating information from multiple data providers. This series is based on firm-level payroll data from Paychex and Intuit, worker-level data on employment and earnings from Earnin, and firm-level timesheet data from Kronos. This data is compiled by Opportunity Insights. Data notes from Opportunity Insights: Data Source: Paychex, Intuit, Earnin, Kronos Update Frequency: Weekly Date Range: January 15th 2020 until the most recent date available. The most recent date available for the full series depends on the combination of Paychex, Intuit and Earnin data. We extend the national trend of aggregate employment and employment by income quartile by using Kronos timecard data and Paychex data for workers paid on a weekly paycycle to forecast beyond the end of the Paychex, Intuit and Earnin data. Data Frequency: Daily, presented as a 7-day moving average Indexing Period: January 4th - January 31st Indexing Type: Change relative to the January 2020 index period, not seasonally adjusted. More detailed documentation on Opportunity Insights data can be found here: https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.pdf

The ATO (Australian Tax Office) made a dataset openly available (see links) showing all the Australian Salary and Wages (2002, 2006, 2010, 2014) by detailed occupation (around 1,000) and over 100 SA4 regions. Sole Trader sales and earnings are also provided. This open data (csv) is now packaged into a database (*.sql) with 45 sample SQL queries (backupSQL[date]_public.txt).See more description at related Figshare #datavis record. Versions:V5: Following #datascience course, I have made main data (individual salary and wages) available as csv and Jupyter Notebook. Checksum matches #dataTotals. In 209,xxx rows.Also provided Jobs, and SA4(Locations) description files as csv. More details at: Where are jobs growing/shrinking? Figshare DOI: 4056282 (linked below). Noted 1% discrepancy ($6B) in 2010 wages total - to follow up.#dataTotals - Salary and WagesYearWorkers (M)Earnings ($B) 20028.528520069.4372201010.2481201410.3584#dataTotal - Sole TradersYearWorkers (M)Sales ($B)Earnings ($B)20020.9611320061.0881920101.11122620141.19630#links See ATO request for data at ideascale link below.See original csv open data set (CC-BY) at data.gov.au link below.This database was used to create maps of change in regional employment - see Figshare link below (m9.figshare.4056282).#packageThis file package contains a database (analysing the open data) in SQL package and sample SQL text, interrogating the DB. DB name: test. There are 20 queries relating to Salary and Wages.#analysisThe database was analysed and outputs provided on Nectar(.org.au) resources at: http://118.138.240.130.(offline)This is only resourced for max 1 year, from July 2016, so will expire in June 2017. Hence the filing here. The sample home page is provided here (and pdf), but not all the supporting files, which may be packaged and added later. Until then all files are available at the Nectar URL. Nectar URL now offline - server files attached as package (html_backup[date].zip), including php scripts, html, csv, jpegs.#installIMPORT: DB SQL dump e.g. test_2016-12-20.sql (14.8Mb)1.Started MAMP on OSX.1.1 Go to PhpMyAdmin2. New Database: 3. Import: Choose file: test_2016-12-20.sql -> Go (about 15-20 seconds on MacBookPro 16Gb, 2.3 Ghz i5)4. four tables appeared: jobTitles 3,208 rows | salaryWages 209,697 rows | soleTrader 97,209 rows | stateNames 9 rowsplus views e.g. deltahair, Industrycodes, states5. Run test query under **#; Sum of Salary by SA4 e.g. 101 $4.7B, 102 $6.9B#sampleSQLselect sa4,(select sum(count) from salaryWageswhere year = '2014' and sa4 = sw.sa4) as thisYr14,(select sum(count) from salaryWageswhere year = '2010' and sa4 = sw.sa4) as thisYr10,(select sum(count) from salaryWageswhere year = '2006' and sa4 = sw.sa4) as thisYr06,(select sum(count) from salaryWageswhere year = '2002' and sa4 = sw.sa4) as thisYr02from salaryWages swgroup by sa4order by sa4

In this essay, I begin by identifying the reasons that automation has not wiped out a majority of jobs over the decades and centuries. Automation does indeed substitute for labor—as it is typically intended to do. However, automation also complements labor, raises output in ways that leads to higher demand for labor, and interacts with adjustments in labor supply. Journalists and even expert commentators tend to overstate the extent of machine substitution for human labor and ignore the strong complementarities between automation and labor that increase productivity, raise earnings, and augment demand for labor. Changes in technology do alter the types of jobs available and what those jobs pay. In the last few decades, one noticeable change has been a "polarization" of the labor market, in which wage gains went disproportionately to those at the top and at the bottom of the income and skill distribution, not to those in the middle; however, I also argue, this polarization is unlikely to continue very far into future. The final section of this paper reflects on how recent and future advances in artificial intelligence and robotics should shape our thinking about the likely trajectory of occupational change and employment growth. I argue that the interplay between machine and human comparative advantage allows computers to substitute for workers in performing routine, codifiable tasks while amplifying the comparative advantage of workers in supplying problem-solving skills, adaptability, and creativity.

Analysis of ‘Baltimore City Employee Salaries’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/09dd5c6e-6cc5-4322-adf7-8c0a6616dbc7 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset includes Baltimore City employee salaries and gross pay from fiscal year 2011 through last fiscal year and includes employees who were employed on June 30 of the last fiscal year. For fiscal years 2020 and prior, data are extracted from the ADP payroll system. For fiscal year 2021, the data are combined from the ADP system and the Workday enterprise resource planning system which now includes payroll.

Change Log
2021-10-29:
- Added FY2021 data
- Metadata added
- Columns renamed to a standard format
- Youth workers not employed by City removed
- Agency names reformatted with Workday conventions

Data Dictionary

--- Original source retains full ownership of the source dataset ---

The Occupational Employment and Wage Statistics (OEWS) survey is a semiannual mail survey of employers that measures occupational employment and occupational wage rates for wage and salary workers in nonfarm establishments, by industry. OEWS estimates are constructed from a sample of about 41,400 establishments. Each year, forms are mailed to two semiannual panels of approximately 6,900 sampled establishments, one panel in May and the other in November.

There are many factors that could influence the choice of changing workplace, but the economic aspect seems to be the most relevant one among Italian employees. According to a survey conducted by JobPricing in Italy in 2023, a ************ ranked first among the main aspects considered when changing a job. In fact, ***** in *** surveyed employees would leave their current workplace for a more *************. ** percent of the interviewees would change their profession for better ********************, while ********* of them considered relevant a good ***************** for getting a new position. Less common reasons for leaving a job were a better working space and non-monetary benefits.

This dataset is a listing of all active City of Chicago employees, complete with full names, departments, positions, employment status (part-time or full-time), frequency of hourly employee –where applicable—and annual salaries or hourly rate. Please note that "active" has a specific meaning for Human Resources purposes and will sometimes exclude employees on certain types of temporary leave. For hourly employees, the City is providing the hourly rate and frequency of hourly employees (40, 35, 20 and 10) to allow dataset users to estimate annual wages for hourly employees. Please note that annual wages will vary by employee, depending on number of hours worked and seasonal status. For information on the positions and related salaries detailed in the annual budgets, see https://www.cityofchicago.org/city/en/depts/obm.html

Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)