The vision for Skillenai has always been to build tools that allow users to identify and fill their skill gaps. Now that a robust data science article aggregation service has been completed, Skillenai is ready for the next step on that journey. Here’s what the Skill Gap product will look like when completed.
- User uploads resume to be parsed and embedded.
- Zero-shot job recommendations generated by pulling from a jobs search index based on similarity of resume and job embeddings, along with search clauses utilizing parsed fields.
- User selects jobs from list of recommendations as target jobs.
- Skill Gap Engine runs on each pair of resume, target job to determine frequent skill gaps.
- Skill gaps used to recommend articles, courses, and other educational materials.
Skill Gap Engine
- Useful as a standalone tool to run on every job when users are doing job searches.
- Works by comparing skills, experience, and education in resume to those stated in job requirements.
- When searching for jobs, users can see exactly what gaps they have before deciding whether to apply.
Job Data Collection
- Start with service that scrapes Google Jobs: https://serpapi.com/blog/scrape-google-jobs-organic-results-with-python/
- Eventually scrape job postings directly using sitemaps and structured JobPosting markup
- Scrape on a regular schedule, enrich, and store in an Open Search index (just like blog articles)
- Build search tool for users to search collected job posts
- Integrate job recommendations into email digests