Enhancing Occupational Classification and Seniority Prediction from Job Descriptions Using Open-Source Tools and Embedding Techniques

In an era where the job market is constantly evolving, accurately classifying occupations is essential for understanding labor trends, informing policy decisions, and guiding workforce development. The Standard Occupational Classification (SOC) system plays a vital role in organizing job data, which informs policy decisions, supports workforce development, and aids businesses and job seekers alike. However, the complexity and diversity of modern job descriptions make assigning the correct SOC codes a significant challenge.
This talk explores how open-source artificial intelligence tools—specifically PyTorch and models from HuggingFace—can significantly improve the accuracy of SOC code predictions from job descriptions. By harnessing these accessible and community-driven resources, we've developed innovative methods to better interpret and classify unstructured job data.
Attendees will gain insights into:
- The critical role of accurate SOC classification in understanding labor market trends and supporting economic research.
- Challenges in processing complex and varied job descriptions, and how open-source AI tools can overcome them.
- Practical applications of PyTorch and Hugging Face models in building advanced natural language processing solutions for occupational classification.
By demonstrating the power and potential of open-source AI in real-world problems, this session aims to inspire participants to use these tools in their own projects. We'll share our experiences and methodologies, highlighting how collaborative efforts and open-source technologies can address real-world challenges effectively.