Quantitative Analysis Using Random Forests

I'm looking for an expert in machine learning and quantitative analysis to help me analyze various stock performance metrics using the Random Forests technique. The primary goal is to identify which financial ratios are most indicative of stock performance.

Key Tasks:
- Analyze a comprehensive range of stock performance metrics, including but not limited to:
- Price-to-Earnings Ratio (P/E)
- Return on Equity (ROE)
- Earnings Per Share (EPS)
- Debt-to-Equity Ratio
- Dividend Yield
- Free Cash Flow
- Develop an algorithm that outputs a numerical score indicating how indicative a particular metric is for a specific stock. The algorithm should allow for the selection of different stock tickers.

Ideal Skills:
- Proficiency in machine learning, particularly with the Random Forests technique
- Strong background in quantitative analysis
- Experience in financial analysis and understanding of stock performance metrics
- Ability to develop user-friendly algorithms and software

Experience with similar projects will be highly regarded. Please include relevant examples of your work in your bid.