Thomas Kilgore

Lead the design and implementation of large-scale data platforms supporting critical business and AI initiatives. Architect scalable data pipelines and storage processing billions of records, and establish data governance, observability, lineage, and quality monitoring frameworks across teams.

Architected and developed core data infrastructure for Reinforcement Learning from Human Feedback (RLHF) workflows used to train and evaluate frontier large language models. Built scalable ETL and data processing pipelines with data quality/validation frameworks, and implemented monitoring and observability tooling to improve reliability.

Led a multidisciplinary engineering team building predictive analytics products for global commodity and maritime trade intelligence. Architected geospatial processing systems for 100M+ location events per day, directed machine learning forecasting for commodity flows, and built cloud-native data platforms to deliver actionable business intelligence.

Founded and operated a data analytics consulting firm delivering software engineering, machine learning, and data platform solutions for enterprise clients. Led full-cycle client engagements and designed enterprise analytics platforms, including audience intelligence systems linking television advertising exposure with consumer behavior across social media platforms.

Designed and developed scalable data pipelines supporting analytics, reporting, and product development initiatives. Improved data reliability and accessibility through enhanced data modeling and pipeline architecture, and implemented monitoring and validation processes to reduce data quality issues and operational overhead.

Led development of a business intelligence platform with information on more than 22 million small businesses. Architected data acquisition, enrichment, and processing systems, managed engineering initiatives across data collection, machine learning, and quality assurance, and developed anomaly detection algorithms to identify data quality issues.

Built predictive machine learning models using decision trees, random forests, and logistic regression, including work that increased sales conversion rates by more than 9% for a Fortune 500 client. Designed entity-resolution algorithms to match and reconcile business records across large-scale data sources and presented analytical findings to technical and executive stakeholders.

Delivered analytical solutions supporting supply chain optimization initiatives for enterprise clients. Analyzed transportation, logistics, and operational data to identify efficiency and cost-saving opportunities and developed reporting frameworks and performance dashboards for client stakeholders.

Master of Engineering (M.Eng.) in Operations Research and Industrial Engineering at Cornell University from 2005 to 2006.

Bachelor of Science (B.S.) in Operations Research at Cornell University from 2001 to 2005.