This dataset was retrieved from the page https://ai-jobs.net/salaries/download/
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)
skills : Python, AWS, SQL, Pytorch, Tableau
The exploration of global salaries in the fields of Artificial Intelligence (AI), Machine Learning (ML), and Data Science is a crucial undertaking with far-reaching implications. As technology continues to evolve, these domains have become integral components of industries driving demand for skilled professionals. Analyzing salary data across work years, experience levels, employment types, and job titles provides valuable insights into the economic landscape of these rapidly growing sectors. It enables a comprehensive understanding of the financial rewards associated with different roles and experience levels, guiding both job seekers and employers in making informed decisions. Moreover, dataset sheds light on the impact of remote work, allowing us to discern patterns in salary distribution based on employee residence and remote work ratios. By examining company locations and sizes, the study offers a perspective on the geographical and organizational factors influencing compensation. This exploration is essential not only for talent acquisition and retention strategies but also for fostering inclusivity and equitable compensation practices in the dynamic and competitive field of AI, ML, and Data Science.
- How has the average salary in the industry changed over the past few years? And are there specific years that show significant spikes or drops in average salaries? How does the average salary vary based on different experience levels?
- How does the size of the company correlate with salary levels?
- How does salary vary across different locations? Are there regions where salaries are consistently higher or lower than the average?
- How have the in-demand skills for different job titles changed over the years?