• Generated over $120M+ in new revenue by launching ML models to provide smart discounts, detect fraud, and predict churn for SMB customers purchasing Google Workspace, using yggdrasil decision trees and random forests.

• Created a constraint-optimized reinforcement learning model to rank promotional campaigns by their likelihood to drive SKU upgrades, increasing upsell conversion rates by over 25% and generating almost $55M in new revenue alone.

• Launched Trust Rules for Docs & Drive to Beta and GA for Google Workspace, reporting over 1M new paid seats across 7 different SKUs and being awarded language readability for Java within three months.

• Migrated 20+ clients to avoid $400M in fines associated with EU GDPR & DMA compliance by implementing a new client library, identifying technical risks, and managing timelines for three junior engineers.

• Awarded #2 product of the day after launching our MVP to a community of 10K+ followers across platforms.

• Achieved 60%+ MoM user growth and scaled to 500+ customers, providing 30% reduction in time-to-launch for series A & B startups by using AI to automate team collaboration and technical management across their company.

• Enabled automated ML orchestration for 30+ clients to securely deploy, serve, and monitor their models by designing a distributed API authentication framework to validate 10M+ requests per day across 80+ distinct microservices.

• Partnered with multiple teams across Capital One to implement voice biometrics for incoming callers, reducing account takeover (ATO) fraud by over 90% for over 45 million card customers.

• Maintained end-to-end ownership of an event-driven fraud feature calculator, written in Python and Go, utilizing cloud-native architecture and streaming data to provide real-time response to credit card fraud.