Andrew Campbell is a Staff Data Scientist with 12 years of experience building and scaling AI-driven fraud detection and risk forecasting systems for enterprises and startups, including AT&T and an e-commerce IPO. He blends hands-on ML engineering—authoring automated feature engineering pipelines, parallel PySpark training, and Agentic/Graph RAG solutions—with strategic leadership of globally distributed teams and 100+ cross-functional stakeholders. His work has driven dramatic outcomes: up to 80% reductions in fraud, >99% detection precision, 90% fewer false alerts, and a patented portfolio of ML/DB inventions. He’s skilled across the stack (PySpark, TensorFlow/PyTorch, GenAI frameworks, cloud MLOps) and uniquely pairs spatiotemporal and geospatial modeling with product-focused user research (150+ interviews) to align models to business KPIs. Based in Chattanooga, he also has a strong academic grounding in transportation engineering from UC Berkeley and a history of turning research ideas into production tools that cut model development from months to a single day.
12 years of coding experience
13 years of employment as a software developer
Ph.D. Candidate, Transportation Engineering, Ph.D. Candidate, Transportation Engineering at University of California, Berkeley
Master of Science - MS, Transportation Engineering, Master of Science - MS, Transportation Engineering at University of Tennessee, Knoxville
Contributions:8 pushes, 1 branch in 2 years 4 months
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