Project Pi

People with many goals often struggle to manage their time and measure their growth long-term. [Project Pipeline] aims bring more clarity to everyone navigating the labor-skills market today.

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💡Inspiration

We were inspired by the rapidly shifting job market and how the adoption of new technologies is forever altering traditional roles. Upskilling and staying relevant is the ongoing concern for job-seekers and incumbents, but rather than emphasizing specialization, we believe that gaining transferrable skills

The US government's ONET database is infrequently updated, job ads are inconsistent, role titles alone do not provide sufficient insight into daily responsibilities. We saw the need for an evolving knowledge classification system to navigate the overarching labor market.

🔍What it does

Project Pi uses NLP and ML algorithms to project learning paths and career possibilities based on user interests or current skill sets. By scraping resumes, government job data, and matching skills with potential career directions, we built an ontology of relevant skills, knowledge areas, and tasks—showing where jobs are headed and which areas of study lead to those roles. It’s a real-time snapshot of the labor market and a personal roadmap for lifelong learning for new job-seekers and those seeking a transition alike.

Project Pi is an innovative approach empowering the user to make informed decisions on how to prepare for future opportunities by building transferrable skills now: it suggests short-term goals, sets KPIs to motivate progress, and makes emerging trends in hiring practices more accessible through interactive visuals.

🔧How we built it

Day 1:

  • we brainstormed for the customer-market fit, design plan, and branding
  • dataset search and literature review (ONET, Kaggle)
  • documentation Day 2:
  • schema design, server integration, AI model training (Gemini, MongoDB)
  • individual component creation and wrapper classes (visualizations)
  • FastAPI to connect frontend and backend
  • MongoDB server setup to handle multiple streams of real-time data
  • deployment of webapp on GitHub Pages

⚠️Challenges we ran into

  • We are new to attending hackathons, so working with front end CSS, and learning of Bootstrap!
  • Coffee deficiency

🍰Accomplishments that we're proud of

  • we were able develop an impactful project that also felt personally significant!
  • collaborating on solving problems, P2P teaching, and creating a functional MVP

🧠What we learned

  • how to write API endpoints and firsthand experience in frontend development
  • how to process large datasets and train AI models

🚢What's next for Project Pi

  • Making the front-end more aesthetically appealing and seamless.
  • More advanced dataflows and volume of data.
  • Deeper exploration of NLP techniques, expand user customization experience

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