Random Forest Classifier Development & Automation

I'm looking for a skilled data scientist with a strong background in machine learning, particularly in developing and optimizing Random Forest Classifiers.

Key Responsibilities:
1. Data Handling and Preprocessing:
- Loading historical data and updating with new daily draws.
- Feature engineering including frequency, triplets, etc.

2. Machine Learning Models:
- Developing a primary Random Forest Classifier with hyperparameter tuning.
- Optionally, working with other models such as Gradient Boosting Classifier, Neural Networks (MLP) and Ensemble Models.

3. Prediction and Evaluation:
- Making predictions for future draws.
- Ranking probable combinations based on model output and evaluating performance on historical data.

4. Automation:
- Setting up a system for automatic dataset updates and model retraining after each draw.
- Generating daily predictions.

Ideal Candidate:
- Extensive experience with data handling and machine learning.
- Proficient in Random Forest Classifier and hyperparameter tuning.
- Skills in Python and relevant ML libraries (e.g., scikit-learn, TensorFlow).
- Experience in setting up automated systems for data science projects.

Please include examples of similar projects you've worked on in your proposal.