Mike Mac Laverty

Mike Mac Laverty

Director Data Science & Machine Learning @ Alight Solutions

About Mike Mac Laverty

Mike Mac Laverty is the Director of Data Science & Machine Learning at Alight Solutions in Atlanta, Georgia. He has extensive experience in data science, machine learning, and database marketing, having previously held various roles at AT&T and Hewitt Associates.

Title

Mike Mac Laverty currently holds the position of Director of Data Science & Machine Learning at Alight Solutions. He directs initiatives in the data science and machine learning domains, leveraging his extensive experience to drive innovative solutions in the Atlanta, Georgia area.

Current Role at Alight Solutions

As Director of Data Science & Machine Learning at Alight Solutions, Mike Mac Laverty leads the development and implementation of advanced machine learning models and data-driven strategies. His work focuses on enhancing operational efficiency and optimizing HR outcomes, while also contributing to AI risk governance frameworks.

Past Roles at AT&T

Before joining Alight Solutions, Mike Mac Laverty had a lengthy tenure at AT&T, holding various key positions from 2005 to 2019, including Senior Data Analyst, Lead Data Analyst, Associate Director, and Director of Database Marketing. His roles involved sophisticated data analytics, marketing strategies, and operational improvements particularly within the Greater Atlanta Area.

Education and Expertise

Mike Mac Laverty earned a Bachelor's degree in Computer and Information Sciences and Finance from the University of Florida, where he studied from 1996 to 2000. His dual major provided a strong foundation that has supported his career in data science, machine learning, and database marketing.

Initiatives and Implementations

Mike Mac Laverty has spearheaded significant projects, including a generative AI chatbot with a multi-RAG architecture, predictive models for call center optimization, and comprehensive design of experiments with control groups and A/B testing. He established a robust ML ecosystem and MLOps infrastructure, including data pipelines and model monitoring in AWS, and played a crucial role in AI risk governance.

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