This dataset provides values for WAGES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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

This dataset provides values for WAGES IN MANUFACTURING reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

####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)

🧠 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

Wages in China increased to 120698 CNY/Year in 2023 from 114029 CNY/Year in 2022. This dataset provides - China Average Yearly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Information on net earnings (net pay taken home, in absolute figures) complements gross earnings data with respect to disposable earnings. The transition from gross to net earnings requires the deduction of income taxes and employee's social security contributions from the gross amounts and the addition of family allowances, if appropriate. The amount of these components and therefore the ratio of net to gross earnings depend on the individual situation (marital status, number of dependent children, and level of gross earnings compared to the average salary). The figures represent the percentage change on the previous period, defined as either the annual amount or the average the last 3 years. The data is based on a widely acknowledged model developed by the OECD, where tax and benefit information is obtained from national sources.

This dataset provides values for WAGE GROWTH reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

1) Data Introduction • The Data Science Salaries 2023 Dataset is a global annual salary analysis dataset that summarizes a variety of information in a tabular format, including salary, career, employment type, job, remote work rate, and company location and size for data science jobs as of 2023.

2) Data Utilization (1) Data Science Salaries 2023 Dataset has characteristics that: • Each row contains 11 key characteristics, including year, career level, employment type, job name, annual salary (local currency and USD), employee country of residence, remote work rate, company location, and company size. • Data is organized to reflect different countries, jobs, careers, and work patterns to analyze pay and work environments in data science in three dimensions. (2) Data Science Salaries 2023 Dataset can be used to: • Data Science Salary Analysis and Comparison: Analyzing salary levels and distributions by job, career, country, and company size can be used to understand industry trends and market value. • Establishing Recruitment and Career Strategies: It can be applied to recruitment strategies, career development, global talent attraction, etc. by analyzing the correlation between various working conditions and salaries such as remote work rates, employment types, and company location.

Kenya Average Wage Earnings data was reported at 894,232.800 KES in 2023. This records an increase from the previous number of 864,750.100 KES for 2022. Kenya Average Wage Earnings data is updated yearly, averaging 617,900.550 KES from Jun 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 894,232.800 KES in 2023 and a record low of 366,613.600 KES in 2008. Kenya Average Wage Earnings data remains active status in CEIC and is reported by Kenya National Bureau of Statistics. The data is categorized under Global Database’s Kenya – Table KE.G009: Average Wage Earnings: by Sector and Industry: International Standard of Industrial Classification Rev 4.

Avg Monthly Wage per Employee: Elementary Occupations data was reported at 343.000 JOD in 2016. This records an increase from the previous number of 315.000 JOD for 2015. Avg Monthly Wage per Employee: Elementary Occupations data is updated yearly, averaging 222.000 JOD from Oct 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 343.000 JOD in 2016 and a record low of 137.000 JOD in 2000. Avg Monthly Wage per Employee: Elementary Occupations data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Jordan – Table JO.G020: Average Monthly Wage per Employee.

Belgium Average Monthly Salary: Age: 35 to 39 data was reported at 3,834.000 EUR in 2021. This records an increase from the previous number of 3,783.000 EUR for 2020. Belgium Average Monthly Salary: Age: 35 to 39 data is updated yearly, averaging 3,136.000 EUR from Sep 1999 (Median) to 2021, with 23 observations. The data reached an all-time high of 3,834.000 EUR in 2021 and a record low of 2,289.000 EUR in 1999. Belgium Average Monthly Salary: Age: 35 to 39 data remains active status in CEIC and is reported by Directorate-General Statistics - Statistics Belgium. The data is categorized under Global Database’s Belgium – Table BE.G036: Average Monthly Salary.

Pakistan Average Monthly Wages data was reported at 24,028.000 PKR in 2021. This records an increase from the previous number of 21,326.000 PKR for 2019. Pakistan Average Monthly Wages data is updated yearly, averaging 12,636.500 PKR from Jun 2008 (Median) to 2021, with 10 observations. The data reached an all-time high of 24,028.000 PKR in 2021 and a record low of 6,612.000 PKR in 2008. Pakistan Average Monthly Wages data remains active status in CEIC and is reported by Pakistan Bureau of Statistics. The data is categorized under Global Database’s Pakistan – Table PK.G004: Average Monthly Wages: by Industry. No data for 2016-2017 as per source. Labour Force Survey has not been conducted for these two years due to Population Census.

Latvia Average Wages and Salaries: Gross: AR: Gambling and Betting Activities data was reported at 1,184.340 EUR in Jun 2018. This records an increase from the previous number of 1,032.700 EUR for May 2018. Latvia Average Wages and Salaries: Gross: AR: Gambling and Betting Activities data is updated monthly, averaging 759.345 EUR from Jan 2008 (Median) to Jun 2018, with 126 observations. The data reached an all-time high of 1,184.340 EUR in Jun 2018 and a record low of 558.460 EUR in Feb 2010. Latvia Average Wages and Salaries: Gross: AR: Gambling and Betting Activities data remains active status in CEIC and is reported by Central Statistical Bureau of Latvia. The data is categorized under Global Database’s Latvia – Table LV.G018: Average Wages and Salaries: Statistical Classification of Economic Activities Revision 2.

MLB Players Salaries And Performance

Analyzing Salaries, Contract Length, and Team Performance

By Nate Reed [source]

About this dataset

This dataset contains information about Major League Baseball players’ salaries and contracts, sourced from USA Today. It includes information like the player's salary for the current season, total contract value, position they play, number of years their contract is for and average annual salary. This dataset allows you to explore MLB player contracts at a deeper level, examine differences between players' salaries across different positions and teams, identify which teams are paying their players the most per annum or over the duration of full contracts

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides detailed salary and contract information for Major League Baseball players. It contains all the most up-to-date information about each player's contract, including salary, total value, position, years, average annual salary, and team affiliation. With this data you can analyze trends in player salaries and contracts to identify opportunities for maximizing profits.

You can also use this data to compare the relative worth of players at different positions across teams. Use it to research trade value of players - including estimated trade values based on their contracts - as well as provide statistical analysis of the effects that player moves have had on teams' success. Additionally, you can utilize it to build predictive models that use past contracts to predict future salary increases or decreases when negotiating new contracts with existing or prospective players.

Ready to get started? Here are a few tips on how best to utilize this dataset: - Examine the Total Value column first since it is often a key indicator in determining a player's worth; - Look at previous years’ salaries by team for comparision purposes;
- Factor in performance metrics like OPS (on-base plus slugging percentage), ERA (earned run average), WHIP (walks + hits/innings pitched), FIP (fielding independent pitching); - Take into account intangibles such as fan interest/popularity; - Utilize averages across different positions and teams – are certain players way underpaid compared his peers? Conversely are certain overpaid compared his peers? Finding these mismatches could potentially create an arbitrage opportunity if a trade were made.

By understanding how successful teams build rosters using Major League Baseball Player Salaries and Contracts datasets you too can be empowered with data driven decisions when investing in your fantasy baseball team or MLB organization!

Research Ideas

• Analyzing which teams are spending the most on salary, and determining how that is affecting their performance.
• Comparing positions to see which positions earn more money across teams and leagues.
• Identifying trends in salaries for larger contracts vs smaller ones, to help players and teams determine better negotiating strategies for future signings

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: salaries.csv | Column name | Description | |:----------------|:-------------------------------------------------------------| | salary | The amount of money a player is paid for a season. (Numeric) | | name | The name of the player. (String) | | total_value | The total value of the player's contract. (Numeric) | | pos | The position the player plays. (String) | | years | The length of the player's contract. (Numeric) | | avg_annual | The average annual salary of the player. (Numeric) | | team | The team the player plays for. (String) |

Acknowledgements

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

🚀 Data Science Careers in 2025: Jobs and Salary Trends in Pakistan 🚀 Data Science is one of the fastest-growing fields, and by 2025, the demand for skilled professionals in Pakistan will only increase. If you’re considering a career in Data Science, here’s what you need to know about the top jobs and salary trends.

🔍 Top Data Science Jobs in 2025 1) Data Scientist Avg Salary: PKR 1.2M - 2.5M/year (Entry-Level), PKR 3M - 6M/year (Experienced) Skills: Python, R, Machine Learning, Data Visualization

2) Data Analyst Avg Salary: PKR 800K - 1.5M/year (Entry-Level), PKR 2M - 3.5M/year (Experienced) Skills: SQL, Excel, Tableau, Power BI

3) Machine Learning Engineer Avg Salary: PKR 1.5M - 3M/year (Entry-Level), PKR 4M - 7M/year (Experienced) Skills: TensorFlow, PyTorch, Deep Learning, NLP

4)Business Intelligence Analyst Avg Salary: PKR 1M - 2M/year (Entry-Level), PKR 2.5M - 4M/year (Experienced) Skills: Data Warehousing, ETL, Dashboarding

5) AI Research Scientist Avg Salary: PKR 2M - 4M/year (Entry-Level), PKR 5M - 10M/year (Experienced) Skills: AI Algorithms, Research, Advanced Mathematic

💡 Why Choose Data Science? High Demand: Every industry in Pakistan needs data professionals. Attractive Salaries: Competitive pay based on technical expertise. Growth Opportunities: Unlimited career growth in this field.

📈 Salary Trends Entry-Level: PKR 800K - 1.5M/year Mid-Level: PKR 2M - 4M/year Senior-Level: PKR 5M+ (depending on expertise and industry)

🛠️ How to Get Started? Learn Skills: Focus on Python, SQL, Machine Learning, and Data Visualization. Build Projects: Work on real-world datasets to create a strong portfolio. Network: Connect with industry professionals and join Data Science communities.

work_year: The year in which the data was recorded. This field indicates the temporal context of the data, important for understanding salary trends over time.

job_title: The specific title of the job role, like 'Data Scientist', 'Data Engineer', or 'Data Analyst'. This column is crucial for understanding the salary distribution across various specialized roles within the data field.

job_category: A classification of the job role into broader categories for easier analysis. This might include areas like 'Data Analysis', 'Machine Learning', 'Data Engineering', etc.

salary_currency: The currency in which the salary is paid, such as USD, EUR, etc. This is important for currency conversion and understanding the actual value of the salary in a global context.

salary: The annual gross salary of the role in the local currency. This raw salary figure is key for direct regional salary comparisons.

salary_in_usd: The annual gross salary converted to United States Dollars (USD). This uniform currency conversion aids in global salary comparisons and analyses.

employee_residence: The country of residence of the employee. This data point can be used to explore geographical salary differences and cost-of-living variations.

experience_level: Classifies the professional experience level of the employee. Common categories might include 'Entry-level', 'Mid-level', 'Senior', and 'Executive', providing insight into how experience influences salary in data-related roles.

employment_type: Specifies the type of employment, such as 'Full-time', 'Part-time', 'Contract', etc. This helps in analyzing how different employment arrangements affect salary structures.

work_setting: The work setting or environment, like 'Remote', 'In-person', or 'Hybrid'. This column reflects the impact of work settings on salary levels in the data industry.

company_location: The country where the company is located. It helps in analyzing how the location of the company affects salary structures.

company_size: The size of the employer company, often categorized into small (S), medium (M), and large (L) sizes. This allows for analysis of how company size influences salary.

This dataset provides values for AVERAGE HOURLY EARNINGS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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 Wage: Guizhou: South Guizhou data was reported at 94,821.000 RMB in 2023. This records an increase from the previous number of 92,881.000 RMB for 2022. Average Wage: Guizhou: South Guizhou data is updated yearly, averaging 57,270.000 RMB from Dec 2005 (Median) to 2023, with 18 observations. The data reached an all-time high of 94,821.000 RMB in 2023 and a record low of 14,054.000 RMB in 2005. Average Wage: Guizhou: South Guizhou data remains active status in CEIC and is reported by South Guizhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Labour Market – Table CN.GP: Average Wage: Prefecture Level Region.

The difference between the earnings of women and men shrank slightly over the past years. Considering the controlled gender pay gap, which measures the median salary for men and women with the same job and qualifications, women earned one U.S. cent less. By comparison, the uncontrolled gender pay gap measures the median salary for all men and all women across all sectors and industries and regardless of location and qualification. In 2025, the uncontrolled gender pay gap in the world stood at 0.83, meaning that women earned 0.83 dollars for every dollar earned by men.