Bruce Bugbee

Senior Manager, Machine Learning Science

Brought in to start up the machine learning function within Airtable's Data team, building advanced solutions using AI and ML capabilities. Focused on establishing the foundational infrastructure needed to productionize ML and LLM systems at scale—from model training and experimentation platforms to batch inference pipelines and serving infrastructure.

Key achievements:

• ›Built end-to-end ML Ops infrastructure: model training, experimentation, serving, and batch LLM inference capabilities
• ›Shipped first production ML model improving marketing lead-scoring accuracy by ~50%, directly impacting pipeline quality

Senior Applied Scientist

Led science efforts around Amazon's incrementality measurement platform, with emphasis on developing improved causal attribution models for advertising across channels on Amazon. Focused on helping advertisers understand true campaign effectiveness through rigorous causal inference and experimentation.

Key achievements:

• ›Led cross-disciplinary science team owning the advertiser-facing RCT experimentation platform
• ›Established technical standards and best practices for causal ML models powering Multi-Touch Attribution products
• ›Developed strategic roadmap for extending causal inference capabilities across international marketplaces
• ›Built evaluation frameworks for measuring attribution model quality and business impact

VP of Data Science

Brought on after CaliberMind's Series A to handle all things data in an early-stage startup environment. The core challenge: building unified data abstractions that could integrate each B2B customer's unique data structure into a common analytical framework. Led everything from data connection strategy and metric definition to attribution modeling and analytics infrastructure.

Key achievements:

• ›Designed analytical abstractions and unified data layers that allowed diverse B2B customer data to integrate seamlessly into the attribution platform
• ›Built ML models for multi-touch attribution that worked across varied customer data structures and marketing touchpoints
• ›Established foundational data practices around connection management, metric definition, and data integrity

Principal Data Scientist → Director, Data Science

I spent nearly four years with the audience modeling group at Oracle Advertising (formerly Oracle Data Cloud), where we built ML systems that turned diverse data sources—online behavior, purchase data, demographics—into 3rd party audiences for advertisers. I was a full-stack data scientist doing everything: model development, large-scale data engineering, data quality alerting, DevOps, the works. The team eventually expanded into contextual classification for open web advertising. Started as a Principal Data Scientist and worked my way up to Director.

Key achievements:

• ›Led ML systems for audience targeting and activation across Oracle's advertising products
• ›Modernized legacy platforms delivering $400K annual cost savings, ~30% performance lift, and 10× runtime improvements
• ›Built shared ML infrastructure enabling reliable model deployment, monitoring, and A/B testing across teams
• ›Managed cross-functional data science team while maintaining hands-on technical leadership

Computational Statistician

Got to work on a wide array of data problems across renewable energy fields—energy systems modeling, high-performance computing optimization, transportation analytics, and more. This is where I learned to take complex statistical methods and make them useful for domain experts.

Key research:

• ›Applied machine learning and statistical methods to energy system optimization, HPC power prediction, and vehicle telematics analysis
• ›Published research on interactive visualization combined with deep learning for real-time energy model exploration
• ›Analyzed telematics data from 57,000+ vehicles traveling 210M+ miles to assess truck platooning feasibility

Postdoctoral Researcher, Biostatistics

Worked on problems in functional data analysis with applications to cancer medicine and RNA data. First position after finishing my PhD, applying computational statistics to real medical research.