Predict Player Efficiency with Random Forest

I'm looking for a data analyst with experience in machine learning and regression analysis, specifically using Random Forest, to predict player performance, specifically the Player Efficiency Rating (PER).

Data Type:
- Game statistics: We have comprehensive records of players' game performances.
- Biometric data: This includes physical measurements and health indicators, relevant to performance prediction.

Data Frequency:
- Our data is updated daily, providing a dynamic and current dataset for analysis.

The ideal candidate should:
- Have a strong background in data science and machine learning
- Be proficient in using Random Forest for regression analysis
- Have experience working with sports data
- Be able to deliver insightful predictions on player efficiency

This project requires an individual who can not only perform the analysis but also interpret the results in a way that's understandable and useful for our needs.