Machine Learning Engineer
Boulder, Colorado, United States
As part of the MagNav project, I focused on enhancing the accuracy of military-grade magnetic navigation systems. My primary responsibility was to develop a machine learning algorithm that leverages global geophysical predictor grids to predict magnetic anomaly grids, a crucial data source for the MagNav navigation algorithms.Given the limited availability of high-quality magnetic data, I employed advanced data science techniques to aggregate, clean, and analyze diverse geophysical datasets, thereby improving the predictive capability of our models. My work involved creating and optimizing algorithms in Python, utilizing the numpy ecosystem for efficient data manipulation, and ensuring the seamless integration of our predictive models into the larger MagNav framework.This role allowed me to apply my expertise in data science, geophysics, and machine learning to a real-world challenge, contributing to the development of innovative navigation solutions for NOAA and its stakeholders.