Amazon Relay Load Booking Optimization

I need an AI-savvy analyst who already understands how the Amazon Relay app works—either from direct fleet usage or previous experience inside Amazon logistics. My immediate goal is to uncover meaningful load patterns hidden in Relay’s historical data and then turn those insights into a smoother, higher-yield booking routine.

Here’s what the engagement looks like:
• Pull and clean my existing Relay load records (CSV exports & API calls are available).
• Train machine-learning models—your choice of algorithms as long as they surface actionable load patterns, seasonalities, and lane behaviours.
• Translate those findings into concrete booking rules or an automated script that helps me grab the most profitable loads faster, with minimal manual refreshing.

Acceptance criteria
1. A short technical report showing which features drive load availability and RPM swings, supported by model visualisations or explainers.
2. A working prototype (Jupyter notebook, Python script, or similar) that ranks live loads in real time according to the discovered patterns.
3. Clear next-step recommendations for scaling the solution or integrating it back into the Relay workflow.

If this aligns with your Amazon Relay know-how and machine-learning skill set, let’s discuss timelines and any access you’ll need.