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.
Onboarding Workflow
- 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)
Job Search
- Build search tool for users to search collected job posts
- Integrate job recommendations into email digests
