AT A GLANCE
Self-reported salary data from over 62,000 employees at leading tech companies including Apple, Amazon, Microsoft, and Google. Examines how demographic and educational variables affect compensation in STEM and data science roles.
Key Highlights:
- Records: 62,000+ salary entries
- Companies: Apple, Amazon, Microsoft, Google, and others
- Variables: Salary, gender, race, education, experience, role type, free-response fields
- Strengths: Large sample, diverse variable types, suitable for correlation and regression analysis
PROJECT DESCRIPTION
This dataset is particularly valuable for investigating structural pay inequities in STEM. It includes controllable factors (years of experience, education level) and uncontrollable factors (gender, race) alongside detailed compensation data. The variety of variable types supports multiple analytical approaches from descriptive statistics to machine learning.
Research Applications:
- Gender and racial pay gap analysis in STEM
- Regression modeling of compensation determinants
- Company-level salary benchmarking
- Text coding exercises using free-response variables
Subject Terms: STEM salaries, data science, gender pay gap, racial disparities, technology industry, compensation, United States
SCOPE & METHODOLOGY
- Geographic Coverage: United States (primarily)
- Smallest Geographic Unit: Individual respondent
- Time Period: See data files for specific range
- Universe: Employees in STEM and data science roles at U.S. technology companies
- Unit of Observation: Individual employee salary record
- Data Type: Self-reported survey data
Data Collection: Salary data self-reported by employees at major technology companies. Covers demographic characteristics, educational background, and multiple compensation components.
CITATION
University of Maryland, College of Behavioral and Social Sciences [distributor]. Data Science and STEM Salaries. BSOS Social Science Data Repository, 2026. https://bsos-data.umd.edu/dataset/data-science-and-stem-salaries
FILES & DOCUMENTATION
Available:
- Data Science and STEM Salaries README (DOCX)
Planned Additions:
- Data Dictionary — Company codes, role categories, education levels, compensation components
- Codebook — Variable listing with value labels for categorical fields
- Data File (CSV/XLSX) — Primary dataset