AI-Powered Job Market Dynamics

Dataset Features:

1. Job_Title:
   • Description: The specific designation or title associated with the job role.
   • Type: Categorical
   • Example Values: “Data Scientist”, “Software Engineer”, “HR Manager”, “AI Researcher”, “Automation Specialist”
2. Industry:
   • Description: The industry sector to which the job belongs.
   • Type: Categorical
   • Example Values: “Technology”, “Healthcare”, “Finance”, “Manufacturing”, “Retail”
3. Company_Size:
   • Description: The scale or size of the company that is offering the job position.
   • Type: Categorical
   • Categories: “Small”, “Medium”, “Large”, “Enterprise”
4. Location:
   • Description: The geographical location where the job is based.
   • Type: Categorical
   • Example Values: “New York”, “San Francisco”, “London”, “Berlin”, “Tokyo”
5. AI_Adoption_Level:
   • Description: The degree to which the company has integrated AI into its business processes.
   • Type: Categorical
   • Categories: “Low”, “Medium”, “High”, “Pioneering”
6. Automation_Risk:
   • Description: The predicted likelihood that the job role could be automated within the next decade.
   • Type: Categorical
   • Categories: “Low”, “Medium”, “High”, “Critical”
7. Required_Skills:
   • Description: The key competencies and skills necessary for performing the job role effectively.
   • Type: Categorical
   • Example Values: “Python”, “Data Analysis”, “Project Management”, “Machine Learning”, “Cloud Computing”