Truck Income Risk ML Mentor

I’m building a web platform that estimates how risky a truck’s income is and I need a seasoned machine-learning mentor to steer me through the full pipeline. The goal is to create a Python/XGBoost model that can simultaneously deliver three perspectives on risk:

• the chance that income falls below a chosen amount
• the expected drop in income over a future period
• an overall risk label (low / medium / high)

To achieve this, I am looking for someone with expertise in the following areas:

1. Python, pandas, scikit-learn, XGBoost, time series analysis, handling imbalanced data, and robust model evaluation.
2. Finding relevant datasets or generating synthetic data where gaps exist—for a specific country/region.
3. Building retrieval-based AI assistants capable of providing answers grounded in user-provided data.
4. Full-stack web development with skills in database design, APIs, and integrating funding and payment logic.
5. Software design principles including UML diagrams, testing strategies, and comprehensive documentation.
6. Academic writing to guide me through interim reports, literature reviews, methodology articulation, and proper referencing standards.
7. Clear mentoring to help me understand every step so I can confidently explain and defend all decisions to my examiners.

You’ll meet with me regularly to review my code and experiments, providing strategic guidance, best practices, and critical feedback while ensuring academic rigor in the final outcome. I'll handle the hands-on coding; you’ll supply the expertise and mentorship needed throughout the process.

When you reply, please outline:
1. Relevant experience in Python/XGBoost/data synthesis.
2. Prior mentoring or advisory work.
3. Your hourly rate and time availability per week.

Looking forward to collaborating closely with someone who can make this graduation project insightful, rigorous, and submission-ready.