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.

espejelomar/data-science-job-salaries dataset hosted on Hugging Face and contributed by the HF Datasets community

Context:

This dataset provides information on salaries for various data science roles across different countries, experience levels, and company sizes. It includes details such as job titles, employment types, and salary figures in both local and standardized currencies. The data helps analyze trends and patterns in compensation within the global data science industry.

Feature Distribution:

work_year – Contains years (e.g., 2020–2024), showing data across multiple years.

experience_level – Includes categories such as EN (Entry), MI (Mid), SE (Senior), and EX (Executive).

employment_type – Common types: FT (Full-time), PT (Part-time), CT (Contract), FL (Freelance).

job_title – Various roles like Data Scientist, Data Engineer, Machine Learning Engineer, Analyst, etc.

salary – Numeric values representing annual salary in local currency.

salary_currency – Includes currencies such as USD, EUR, INR, GBP, etc.

salary_in_usd – Salary standardized in USD for comparison.

employee_residence – Country codes (e.g., US, IN, GB, DE) showing employee locations.

remote_ratio – Percentage of remote work (0 = Onsite, 50 = Hybrid, 100 = Fully Remote).

company_location – Country codes showing the company’s location.

company_size – Categories: S (Small), M (Medium), L (Large).

Data Science Fields Salary Categorization Dataset contains 9 columns :- | Dimension | Description | | --- | --- | | Working Year | The year the salary was paid ( 2020, 2021, 2022 ) | | Designation | The role worked in during the year | | Experience | The experience level in the job during the year. [ EN - Entry level / Junior, MI - Mid level / Intermediate, SE - Senior level / Expert, EX - Executive level / Director ]| | Employment Status | The type of employment for the role. [ PT - Part time, FT - Full time, CT - Contract, FL - Freelance ]| | Salary In Rupees | The total gross salary amount paid. | | Employee Location | Employee's primary country of residence in during the work year as an ISO 3166 country code.( PFB Link to ISO 3166 country code ) | | Company Location | The country of the employer's main office or contracting branch. | | Company Size | The median number of people that worked for the company during the year. [ S(small) - Less than 50 employees , M(medium) - 50 to 250 employees , L(large) - More than 250 employees ]| | Remote Working Ratio | The overall amount of work done remotely. [ 0 - No Remote Work (less than 20%), 50 - Partially Remote, 100 - Fully Remote (more than 80%) ]|

I have collected the data from ai-jobs.net & modified it for my own convenience Original Data Source - https://salaries.ai-jobs.net/download/ ISO 3166 Country Code - https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes

💲Data Science Job Postings with Salaries (2025)

📌 Overview

This dataset contains data science job postings with salary information, collected and processed in 2025. It is designed for projects in salary prediction, job market analytics, and machine learning research.

📑 Column Descriptions

• job_title → The main keyword in the title of the posted position (e.g., Data Scientist, ML Engineer).
• seniority_level → Seniority of the role (e.g., Junior, Senior, Lead).
• status → Work arrangement type (Remote, Hybrid, On-site).
• company → Anonymized company identifier.
• location → Job location(s) mentioned in the posting.
• post_date → When the job posting was listed.
• headquarter → Location of the company’s headquarters.
• industry → Industry sector of the company (e.g., Finance, Technology).
• ownership → Ownership type (e.g., Public, Private).
• company_size → Number of employees in the company.
• revenue → Reported revenue of the company (if available).
• salary → Annual salary in Euro, expressed as min-max or a single value
• skills→ Extracted list of required or preferred skills (e.g., Python, SQL, Spark).

✅ Files Included

-**data_science_job_posts_2025.csv**
The main dataset containing processed job postings with features such as job title, company attributes, location, salary information, and required skills.

-**feature_engineering.py**
A packaged feature engineering pipeline. This script can be imported directly into Kaggle notebooks to apply all preprocessing and feature transformations consistently and reproducibly.

💡 Possible Use Cases

• Train machine learning models to predict salaries.
• Perform exploratory data analysis on job markets.
• Study skills in demand across data science roles.
• Build portfolio projects demonstrating end-to-end ML pipelines.

⚖️ License & Usage

This dataset is published for educational and research purposes only.

• It is a processed version of scraped data, with transformations to respect privacy and avoid redistribution of raw proprietary content.
• Do not use it for commercial purposes.
• Attribution is appreciated if used in academic or personal projects.

🔑 Keywords

data science jobs dataset, data scientist salary dataset, machine learning salary prediction, job postings dataset, AI careers 2025, data science job market analysis, Kaggle dataset

It includes job titles, work status (remote, hybrid, on-site), salaries, company size, headquarters location, industry type, and revenue. It also highlights the skills in demand (e.g., Python, SQL, Spark, AWS, machine learning) along with compensation ranges. To analyze job market trends, skill requirements, and salary benchmarks in the data science field, this dataset, which includes global data science job postings for 2025, includes detailed information about job roles, seniority levels, company profiles, industries, and required technical skills.

** Content**

Job Information

Job Titles (e.g., Data Scientist, ML Engineer)

Seniority Levels (Junior, Senior, Lead, etc.)

Work Status (Remote, Hybrid, On-site)

Posting Date

Company Details

Company Name

Headquarters Location

Industry Type

Ownership (Public / Private)

Company Size

Revenue

Compensation

Salary Information (ranges or exact values)

Skills Required

Programming & Tools (Python, R, SQL, Spark, AWS, etc.)

Machine Learning & Data Science Skills

H-1B visa sponsorship trends for Data Scientist Ii, covering top employers, salary insights, approval rates, and geographic distribution. Explore how job title impacts the U.S. job market under the H-1B program.

Data Science Jobs and Salaries in Karachi

H-1B visa sponsorship trends for Data Scientist, covering top employers, salary insights, approval rates, and geographic distribution. Explore how job title impacts the U.S. job market under the H-1B program.

The average annual salary of a Data Architect in India was estimated to be over two million 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.

This dataset provides a comprehensive collection of internship listings in data science and data analysis, sourced from Glassdoor. It includes detailed information such as internship titles, company names, locations, estimated salaries and company ratings. This dataset is ideal for analyzing trends in internship opportunities, salary ranges, and company preferences in the data science field.

Job Postings Data for Talent Acquisition, HR Strategy & Market Research Canaria’s Job Postings Data product is a structured, AI-enriched dataset that captures and organizes millions of job listings from leading sources such as Indeed, LinkedIn, and other recruiting platforms. Designed for decision-makers in HR, strategy, and research, this data reveals workforce demand trends, employer activity, and hiring signals across the U.S. labor market and enhanced with advanced enrichment models.

The dataset enables clients to track who is hiring, what roles are being posted, which skills are in demand, where talent is needed geographically, and how compensation and employment structures evolve over time. With field-level normalization and deep enrichment, it transforms noisy job listings into high-resolution labor intelligence—optimized for strategic planning, analytics, and recruiting effectiveness.

Use Cases: What This Job Postings Data Solves This enriched dataset empowers users to analyze workforce activity, employer behavior, and hiring trends across sectors, geographies, and job categories.

Talent Acquisition & HR Strategy • Identify hiring trends by industry, company, function, and geography • Optimize job listings and outreach with enriched skill, title, and seniority data • Detect companies expanding or shifting their workforce focus • Monitor new roles and emerging skills in real time

Labor Market Research & Workforce Planning • Visualize job market activity across cities, states, and ZIP codes • Analyze hiring velocity and job volume changes as macroeconomic signals • Correlate job demand with company size, sector, or compensation structure • Study occupational dynamics using AI-normalized job titles • Use directional signals (job increases/declines) to anticipate market shifts

HR Analytics & Compensation Intelligence • Map salary ranges and benefits offerings by role, location, and level • Track high-demand or hard-to-fill positions for strategic workforce planning • Support compensation planning and headcount forecasting • Feed job title normalization and metadata into internal HRIS systems • Identify talent clusters and location-based hiring inefficiencies

What Makes This Job Postings Data Unique

AI-Based Enrichment at Scale • Extracted attributes include hard skills, soft skills, certifications, and education requirements • Modeled predictions for seniority level, employment type, and remote/on-site classification • Normalized job titles using an internal taxonomy of over 50,000 unique roles • Field-level tagging ensures structured, filterable, and clean outputs

Salary Parsing & Compensation Insights • Parsed salary ranges directly from job descriptions • AI-based salary predictions for postings without explicit compensation • Compensation patterns available by job title, company, and location

Deduplication & Normalization • Achieves approximately 60% deduplication rate through semantic and metadata matching • Normalizes company names, job titles, location formats, and employment attributes • Ready-to-use, analysis-grade dataset—fully structured and cleansed

Company Matching & Metadata • Each job post is linked to a structured company profile, including metadata • Records are cross-referenced with LinkedIn and Google Maps to validate company identity and geography • Enables aggregation at employer or location level for deeper insights

Freshness & Scalability • Updated hourly to reflect real-time hiring behavior and job market shifts • Delivered in flexible formats (CSV, JSON, or data feed) and customizable filters • Supports segmentation by geography, company, seniority, salary, title, and more

Who Uses Canaria’s Job Postings Data • HR & Talent Teams – to benchmark roles, optimize pipelines, and compete for talent • Consultants & Strategy Teams – to guide clients with labor-driven insights • Market Researchers – to understand employment dynamics and job creation trends • HR Tech & SaaS Platforms – to power salary tools, job market dashboards, or recruiting features • Economic Analysts & Think Tanks – to model labor activity and hiring-based economic trends • BI & Analytics Teams – to build dashboards that track demand, skill shifts, and geographic patterns

Summary Canaria’s Job Postings Data provides an AI-enriched, clean, and analysis-ready view of the U.S. job market. Covering millions of listings from Indeed, LinkedIn, other job boards, and ATS sources, it includes detailed job attributes, inferred compensation, normalized titles, skill extraction, and employer metadata—all updated hourly and fully structured.

With deep enrichment, reliable deduplication, and company matchability, this dataset is purpose-built for users needing workforce insights, market trends, and strategic talent intelligence. Whether you're modeling skill gaps, benchmarking compensation, or visualizing hiring momentum, this dataset provides a complete toolkit for HR and labor intelligence.

About Canaria Inc. ...

Dataset

This dataset was created by Prithviraj

Contents

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

Graph and download economic data for Employed full time: Wage and salary workers: Computer scientists and systems analysts occupations: 16 years and over: Women (LEU0254690600A) from 2000 to 2010 about analysts, computers, occupation, full-time, females, salaries, workers, 16 years +, wages, employment, and USA.

Graph and download economic data for Employed full time: Wage and salary workers: Computer programmers occupations: 16 years and over (LEU0254477100A) from 2000 to 2024 about computers, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

AI Job Market & Salary Analysis 2025 Dataset

Dataset Overview

This comprehensive dataset contains detailed information about AI and machine learning job positions, salaries, and market trends across different countries, experience levels, and company sizes. Perfect for data science enthusiasts, career researchers, and market analysts for practice purposes.

Dataset Title

Global AI Job Market & Salary Trends 2025: Complete Analysis of 15,000+ Positions

Dataset Description

What's Inside

It includes detailed salary information, job requirements, company insights, and geographic trends.

Key Features: - 15,000+ job listings from 50+ countries - Salary data in multiple currencies (normalized to USD) - Experience level categorization (Entry, Mid, Senior, Executive) - Company size impact analysis - Remote work trends and patterns - Skills demand analysis - Geographic salary variations - Time-series data showing market evolution

Columns Description

Potential Use Cases

1. Salary Prediction Models

   • Build ML models to predict AI job salaries
   • Analyze factors affecting compensation
   • Compare salaries across different locations

2. Market Trend Analysis

   • Track the evolution of AI job market
   • Identify emerging job roles and skills
   • Analyze remote work adoption patterns

3. Career Planning

   • Understand skill requirements for different positions
   • Compare opportunities across countries
   • Plan career progression paths

4. Business Intelligence

   • Company hiring patterns analysis
   • Skills gap identification
   • Market competition insights

5. Geographic Studies

   • Cost of living vs. salary analysis
   • Regional market maturity assessment
   • Immigration pattern correlations

Data Collection Methodology

This is a synthetic dataset created for educational purposes to simulate AI job market patterns. All data is algorithmically generated based on industry research and market trends.

Sample Data Preview

job_id,job_title,salary_usd,experience_level,company_location,remote_ratio
AI001,Senior ML Engineer,145000,SE,United States,50
AI002,Data Scientist,89000,MI,Germany,100
AI003,AI Research Scientist,175000,EX,United Kingdom,0

Kaggle Dataset Tags

#artificial-intelligence #machine-learning #jobs #salary #career #data-science #employment #tech-industry #remote-work #compensation

Dataset Files Structure

ai-job-market-2025/
├── main_dataset.csv (15,247 rows)
├── skills_analysis.csv (skill frequency data)
├── company_profiles.csv (company information)
├── geographic_data.csv (country/city details)
├── time_series.csv (monthly trends)
└── data_dictionary.pdf (detailed documentation)

Acknowledgments

All personal information has been anonymized. This dataset is intended for educational and research purposes.

Discussion Topics for Community Engagement

1. What factors most influence AI job salaries in your analysis?
2. Interesting patterns you've discovered in remote work trends
3. Best visualization techniques for this type of employment data
4. Prediction model results - share your accuracy scores!
5. Geographic insights that surprised you

Suggested Notebook Titles for Analysis

• Predicting AI Salaries: A Complete ML Pipeline
• The Great Remote Work Shift in AI Jobs
• Skills That Pay: What Makes AI Engineers Valuable
• Global AI Talent Migration Patterns
• Company Size vs. Compensation: The AI Edition

*This dat...

TeamStation AI System Report on LATAM IT Salaries 2024 A Comprehensive Analysis of Salary Trends in Latin America’s IT Sector

Introduction The 2024 TeamStation AI Salary Report provides a comprehensive analysis of IT salary structures in 19 Latin American countries, offering scientific insights into compensation trends across various job roles, experience levels, and contract types. This report leverages 1,521 salary records collected from real hiring data, offering the most precise, non-biased compensation insights in the region​

Key Findings 1. Salary Breakdown by Country Three countries lead in IT talent representation:

🇲🇽 Mexico 🇨🇴 Colombia 🇦🇷 Argentina Brazil and Chile also emerge as key players, showcasing robust demand for high-level AI, ML, and DevOps professionals. Meanwhile, Uruguay and Costa Rica provide a cost-effective alternative for high-skilled developers​

1. Role-Based Salary Trends The six most common roles in the LATAM IT market are:

Full-Stack Developer Front-End Developer Back-End Developer App Developer DevOps Engineer Data Engineer Additionally, AI, MLOps, and Cloud Engineers are seeing increasing demand, commanding salaries up to 60% higher than other IT positions​

1. Salary Ranges by Experience Level Salaries vary significantly based on seniority and contract type:

Junior Developers: $10,000 – $30,000 per year Mid-Level Developers: $20,000 – $50,000 per year Senior Developers: $25,000 – $100,000 per year (with some AI engineers exceeding this range) Full-time contracts pay the highest salaries, while freelance engagements have lower total compensation, but can reach premium rates for niche AI/ML expertise​

1. Key Statistical Insights Average salary across all roles: $30,470.02 USD Standard deviation: $56,817.32 USD (showing large variances based on expertise and role) Minimum salary recorded: $500 USD Maximum salary recorded: $800,000 USD Salary percentiles: 25th percentile: $7,000 USD 50th percentile (median): $16,300 USD 75th percentile: $36,000 USD These figures indicate a wide salary stratification, especially for senior roles and AI-related positions​ .

2. Contract Type & Compensation Salaries vary based on contract type:

Full-time developers earn higher base salaries with benefits. Freelancers earn lower annual salaries but some charge premium hourly rates in AI, Cloud, and DevOps. Mid and senior-level engineers prefer full-time contracts for higher pay and stability​ . Regional Salary Insights Highest-paying regions: 🇨🇱 Chile, 🇧🇷 Brazil, 🇲🇽 Mexico. Mid-range salaries: 🇨🇴 Colombia, 🇦🇷 Argentina. Cost-effective hiring: 🇺🇾 Uruguay, 🇨🇷 Costa Rica​ . Strategic Takeaways AI & MLOps engineers are the most expensive to hire in Mexico, Brazil, and Chile. Cloud, DevOps, and AI roles are seeing the fastest growth in salary demand. Best locations for cost-effective hiring: Colombia, Argentina, Uruguay. AI-driven hiring platforms like TeamStation AI reduce time-to-hire and salary mismatches​

This data collection focuses on persons engaged in the fields of natural science, social science, and engineering as well as in related occupations. The aim of the SSE was to gauge the effects of rapid development and innovation in science and technology during recent years on highly trained scientists and engineers in specific target occupations. Variables in this collection, which encompasses both the original 1982 survey data and data from a 1984 follow-up, include formal and supplemental education and training, current employment characteristics, job history, annual salary, and percentage of time devoted to specific work activities. Demographic characteristics include sex, race, Hispanic origin, and marital status.

As of May 2022, data scientists were the highest-earning information technology professionals in the Philippines. As of that period, data scientists had an average annual salary of about 641.28 thousand Philippine pesos. Coming in second were information security analysts with an average annual salary of about 585 thousand Philippine pesos.