Shane McGovern

Background

About Shane McGovern

Transitioning 15+ years of research and data engineering expertise into machine learning engineering, with a proven track record of building scalable data systems that power AI/ML applications: • Architected feature store platform processing 100K+ customer records weekly for Thompson sampling models, reducing feature engineering time from 4 hours to 15 minutes • Built production metadata platform with automated ingestion, accelerating data discovery from hours to seconds for employees • Led cross-functional teams delivering 25+ ETL solutions, enabling 100% team expansion : Python • SQL • Java • Machine Learning • Data Warehousing • ETL Pipelines • Cloud Architecture (AWS/Azure) • Database Administration • Feature Engineering Currently focused on implementing ML algorithms in production environments, with hands-on experience in Thompson sampling, automated feature engineering, and scalable data infrastructure that supports ML model training and deployment: Building ethical, human-centered AI solutions that bridge research insights with real-world applications. I believe the best ML systems combine technical excellence with collaborative leadership. Open to ML engineering opportunities | Let's connect!