DataMatch: AI-Powered Resume Optimizer for Job Seekers and Human Resources

Overview

DataMatch is a sophisticated resume optimization system that leverages AI and machine learning to analyze job listings and help data science professionals tailor their resumes for maximum impact. Using a dataset of 1.2 million LinkedIn job listings and 17 million company profiles, this tool provides data-driven recommendations for resume keywords, skills, and optimizations.

Features

• Intelligent Keyword Analysis: Advanced text analysis of job listings to identify crucial skills and requirements
• ATS Optimization: Ensures resumes are optimized for Applicant Tracking Systems
• Geographic Insights: Maps job opportunities across U.S. cities for both junior and senior positions
• Company-Specific Analysis: Identifies keyword patterns for specific companies and industries
• Experience-Level Targeting: Separate analysis for experienced professionals and recent graduates
• Skills Gap Analysis: Identifies missing skills and provides recommendations

Technical Architecture

The system is built using:

• Python and SQL integration via Databricks
• AWS EC2 for large-scale data processing
• Custom PySpark implementations
• N-gram/bigram text analysis, Machine Learning and Prediction Analysis
• Keyword frequency and co-occurrence analysis
• Topic modeling for job description analysis

Data Processing Pipeline

# Example of data processing flow
1. Initial data ingestion (LinkedIn job listings)
2. Data segmentation (700-800MB chunks)
3. Schema standardization
4. Text analysis and keyword extraction
5. Location data normalization
6. Skills taxonomy mapping

Installation

# Clone the repository
git clone https://github.com/yourusername/DataMatch.git

# Install dependencies
pip install -r requirements.txt

# Configure environment variables
cp .env.example .env

Usage

# Basic usage example
from datamatch import ResumeOptimizer

optimizer = ResumeOptimizer()
results = optimizer.analyze_resume('path_to_resume.pdf')
recommendations = optimizer.get_recommendations()

System Requirements

• Python 3.8+
• Databricks Runtime 7.3 LTS
• Minimum 16GB RAM
• AWS EC2 instance (for large-scale processing)
• PostgreSQL 12+

Performance Metrics

• Successfully processes 1.2M job listings
• Handles 17M company profiles
• Current match rate improvement: ~8%
• Processing time: ~15 minutes for complete analysis

Limitations

• Currently optimized for data science positions
• Limited to senior and associate level positions
• Geographic focus on U.S. markets
• Requires significant computational resources

Future Development

• Integration with additional job platforms (Glassdoor, Indeed)
• Salary data incorporation
• Company culture metrics
• International market expansion
• Real-time processing capabilities

Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Dataset References

• LinkedIn Jobs Dataset (1.2M records)
• Global Companies Dataset (17M records)
• U.S. Bureau of Labor Statistics Data

Team

• Jeff Mathew Sam
• Chris White
• Josh Li
• Lansing Wilson

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• IoT Management Program, Spring 2024
• U.S. Bureau of Labor Statistics
• [Additional acknowledgments]

Contact

For questions and support, please open an issue or contact [team contact information].

Citation

If you use this Project or any part of it in your own project or research, please cite:

@software{DataMatch2024,
  author = {Sam, Jeff Mathew and White, Chris and Li, Josh and Wilson, Lansing},
  title = {DataMatch: AI-Powered Resume Optimizer},
  year = {2024},
  url = {https://github.com/yourusername/DataMatch}
}