Global AI/ML and Data Science Salary Trends

Columns

• work_year: The year in which the salary was paid.
• experience_level: The level of experience held in the job during the year, categorised as Entry-level/Junior (EN), Mid-level/Intermediate (MI), Senior-level/Expert (SE), or Executive-level/Director (EX).
• employment_type: The type of employment for the role, including Part-time (PT), Full-time (FT), Contract (CT), and Freelance (FL).
• job_title: The specific role or position worked in during the year.
• salary: The total gross salary amount paid in its original currency.
• salary_currency: The currency of the paid salary, represented by an ISO 4217 currency code.
• salary_in_usd: The salary amount converted into United States Dollars (USD), calculated using the FX rate divided by the average USD rate for the respective year.
• employee_residence: The employee's primary country of residence during the work year, indicated by an ISO 3166 country code.
• remote_ratio: The overall proportion of work performed remotely, with values indicating no remote work (0, less than 20%), partially remote (50), or fully remote (100, more than 80%).
• company_location: The country where the employer's main office or contracting branch is situated, identified by an ISO 3166 country code.
• company_size: The average number of people employed by the company during the year, classified as Small (S: less than 50 employees), Medium (M: 50 to 250 employees), or Large (L: more than 250 employees).

Distribution

Usage

• Salary benchmarking: Enabling professionals and companies to compare remuneration against global standards.
• Career planning: Helping individuals, especially newbies and those considering a career switch, to understand potential earnings and career progression paths in AI/ML and Data Science.
• Recruitment strategy: Assisting hiring managers and recruiters in setting competitive salary ranges and attracting talent.
• Market analysis: Providing insights for startup founders and businesses to understand compensation expectations in the industry.

Coverage

• Geographic Scope: The dataset offers global coverage, recording both the employee's primary country of residence and the employer's main office location using ISO 3166 country codes. A substantial portion of the data originates from the United States (90%) for both employee residence and company location, followed by Canada (3%).
• Time Range: The data covers salaries from 2020 to 2023, with data points also present for 2024 and 2025 in the sample provided.
• Professional Scope: It includes various experience levels, job titles (e.g., Data Scientist, Data Engineer), types of employment, and company sizes, reflecting diverse segments within the AI/ML and Big Data professional landscape.

License

Who Can Use It

• Newbies and Experienced Professionals: To gain insights into salary expectations and career advancement.
• Hiring Managers and Recruiters: For determining suitable salary bands and attracting talent.
• Startup Founders: To inform decisions on compensation for new hires.
• Individuals Considering a Career Switch: To make informed choices about new professional directions in AI/ML and Big Data.

Dataset Name Suggestions

• Global AI/ML and Data Science Salary Trends
• AI/ML & Data Science Professional Salaries (2020-2025)
• Worldwide Big Data and AI Compensation Data
• AI/ML Industry Salary Benchmarks

Attributes