Optimizing Rail Safety

• Led the development of a predictive model for estimating rail temperatures in Jacksonville, achieving a 1-degree Celsius error for predictions 48 hours into the future.

• Utilized the XGboost model with historical and weather data, engineered key features across 25 locations, and optimized the model for enhanced accuracy.

• Implemented data pipelines using Airflow, ensuring efficient data processing and model deployment in a production environment using Amazon web services(AWS).

• Collaborated cross-functionally to integrate the model into the company's platform, delivering valuable insights and contributing to ongoing improvements.

• Successfully aided railway safety officers in planning safety measures through precise rail temperature predictions.