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.

Graph and download economic data for Employed full time: Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over (LEU0257856200A) from 2011 to 2024 about occupation, jobs, full-time, compensation, benefits, salaries, workers, 16 years +, wages, employment, and USA.

Analysis of ‘Employee Salaries - 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/1fc1fc94-d685-4c4f-9525-6e6079165315 on 26 January 2022.

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

Annual salary information including gross pay and overtime pay for all active, permanent employees of Montgomery County, MD paid in calendar year 2020. This information will be published annually each year.

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

Explore the progression of average salaries for graduates in Data Analyst Engineering from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Data Analyst Engineering relative to other fields. This data is essential for students assessing the return on investment of their education in Data Analyst Engineering, providing a clear picture of financial prospects post-graduation.

🚀 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.

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 ---

Analysis of ‘Employee Salaries - 2019’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/da04581d-180f-4008-bb0d-70d830f9e673 on 27 January 2022.

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

Annual salary information including gross pay and overtime pay for all active, permanent employees of Montgomery County, MD paid in calendar year 2019. This information will be published annually each year.

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

Explore the progression of average salaries for graduates in Specialist In Marketing Management from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Specialist In Marketing Management relative to other fields. This data is essential for students assessing the return on investment of their education in Specialist In Marketing Management, providing a clear picture of financial prospects post-graduation.

Explore the progression of average salaries for graduates in Education, Instructional Specialist from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Education, Instructional Specialist relative to other fields. This data is essential for students assessing the return on investment of their education in Education, Instructional Specialist, providing a clear picture of financial prospects post-graduation.

Graph and download economic data for Employed full time: Wage and salary workers: Air traffic controllers and airfield operations specialists occupations: 16 years and over: Women (LEU0254735000A) from 2000 to 2024 about operating, air travel, travel, occupation, females, full-time, salaries, workers, 16 years +, wages, employment, and USA.

Explore the progression of average salaries for graduates in Facial Specialist from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Facial Specialist relative to other fields. This data is essential for students assessing the return on investment of their education in Facial Specialist, providing a clear picture of financial prospects post-graduation.

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

Explore the progression of average salaries for graduates in Avionics Specialist from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Avionics Specialist relative to other fields. This data is essential for students assessing the return on investment of their education in Avionics Specialist, providing a clear picture of financial prospects post-graduation.

Overview This dataset provides insights into salary distributions across various job classifications, enabling a deeper understanding of compensation trends across industries, experience levels, and geographical locations. It serves as a valuable resource for HR professionals, job seekers, researchers, and policymakers aiming to analyze pay scales, wage gaps, and salary progression trends.

Data Sources The data is aggregated from multiple employment and compensation reports, salary surveys, and publicly available job postings. It has been cleaned, standardized, and structured to ensure consistency and usability for analytical purposes.

Features Job Title: Specific title of the job (e.g., Data Analyst, Software Engineer, Marketing Manager).

Job Classification: Broad category of jobs (e.g., IT, Finance, Healthcare, Education).

Industry: The sector in which the job belongs (e.g., Technology, Banking, Retail).

Experience Level: Categorized as Entry-level, Mid-level, or Senior-level.

Education Requirement: Minimum qualification required for the job role.

Average Salary (INR/USD/Other Currency): The median or mean salary for a particular job classification.

Salary Range: The minimum and maximum salary offered for a role.

Location: Country or region where the job is based.

Employment Type: Full-time, Part-time, Contract, or Remote.

Company Size: Small, Medium, or Large enterprises.

Potential Use Cases Salary Benchmarking: Compare salary expectations across industries and job roles.

Career Planning: Identify lucrative career paths based on salary trends.

Wage Gap Analysis: Examine salary disparities by gender, location, or experience level.

Cost of Living Adjustments: Assess salaries relative to regional economic conditions.

HR and Recruitment Strategies: Optimize compensation packages to attract top talent.

Acknowledgments The dataset is compiled from various salary reports and job market research sources. Special thanks to contributors and organizations providing employment data for analysis.

License This dataset is shared for educational, research, and analytical purposes. Please ensure compliance with relevant data usage policies before any commercial applications.

Get Started The dataset can be explored using Python (Pandas), R, SQL, or visualization tools like Tableau and Power BI. Sample notebooks and analyses are available in the Kaggle notebook section.

United States - Employed full time: Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over: Women was 39.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over: Women reached a record high of 59.00000 in January of 2012 and a record low of 31.00000 in January of 2018. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over: Women - last updated from the United States Federal Reserve on June of 2025.

Salaries of state work force sorted by agency.

United States - Employed full time: Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over: Men was 8.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over: Men reached a record high of 25.00000 in January of 2022 and a record low of 8.00000 in January of 2024. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over: Men - last updated from the United States Federal Reserve on July of 2025.

As of 2022, the median annual salary of a data analyst in the Chinese data and artificial intelligence industry reached 37 thousand yuan. According to the source, junior-level employees in the technology industry gained the most from changing their jobs. In contrast, from the middle-level upwards, the salary increases are much lower after taking a position at a new employer.

Analysis of ‘Baltimore City Employee Salaries’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/e35392a8-b2b3-4748-b696-1637b8e774a0 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 ---

Graph and download economic data for Employed full time: Wage and salary workers: Miscellaneous community and social service specialists occupations: 16 years and over (LEU0254482900A) from 2000 to 2010 about community, miscellaneous, occupation, full-time, salaries, workers, 16 years +, wages, services, employment, and USA.