San Francisco, California, United States
Scikit-Learn Contributor || Apache Airflow Contributor || Deep Learning || Data Science || Statistical Machine Learning || Reinforcement Learning || Algorithm Optimization || Data Mining || Software Delivery || Predictive Modeling || Operations Research Bill worked as Engineering Manager of Machine Learning on the Consumer ML team at PayPal where he led a globally distributed team (SF Bay Area, Denver, NYC, Guatemala City, Singapore) responsible for building ML systems across product verticals (PayPal Shopping, Rewards, Credit, Giving, Marketing, Bill Pay). Bill was a Ph.D. student advised by Ken Goldberg in UC Berkeley’s AUTOLab and BAIR Lab where he researched optimization and statistical learning techniques for distributed cloud robotics. Previously, he studied Math and Computer Science at Pomona College before working on the Analysis and Experimentation team at Microsoft as a software developer. Bill holds a post-graduate degree (MSc.) in Operations Research from UC Berkeley and has worked with clients all across the globe as a leader in data science, software development, data analytics + viz, computational statistics, and machine learning.
Traveled through 20 countries across 4 continents.
Driving understanding and participation in the financial systems of the future. • Led Gauntlet’s Growth Optimization and Lending teams (10 engineers) managing $5M+ in annual client revenue and $100M+ in DeFi liquidity across Uniswap, Compound, NEAR, and Aerodrome. • Designed and deployed machine-learning–driven economic and risk models that optimized capital efficiency, lending parameters, and liquidity provisioning while improving protocol stability and retention. • Built quantitative frameworks for blockchain monetary policy, modeling incentive emissions, token velocity, and liquidity bootstrapping to align on-chain behavior with long-term network health. • Partnered with research and product leads to ship production-grade financial modeling systems and data pipelines that automated risk calibration, simulation, and incentive optimization at scale.
As an Engineering Manager on the Consumer ML team within Global Analytics and Data Science, I led efforts to build ML systems that personalize consumer experiences across product verticals like PayPal Shopping, Rewards, Credit, Giving, Marketing, and Bill Pay. Key Talent Award recipient, March 2023: Employees nominated for this award should display the highest level of judgement, innovative thinking, commitment, and passion for the job.
Designed, built, maintained, and improved the Machine Learning (ML) models and real-time serving infrastructure used by millions of PayPal customers. Built data-intensive, distributed ML services that allow merchants to reach PayPal consumers using state-of-the-art ranking and recommendation algorithms. Scikit-learn committer: https://github.com/scikit-learn/scikit-learn/pull/16539 https://github.com/scikit-learn/scikit-learn/pull/16599 Apache Airflow committer: https://github.com/apache/airflow/pull/5322 Filed U.S. patent application number 16/565459. Developed data pipeline using Python/Airflow to ingest events from Kafka and compute aggregate statistics; designed data model in Athena/Hive consisting of tables and views to provide the data schema requested by stakeholders while ensuring efficient data storage for downstream ML models.
Industrial Engineering and Operations Research (IEOR) PhD student. BAIR and AUTOLab. Optimization & machine learning for robotics.