Building high performance real-time data pipelines to process high-volume card transactions, supporting business operations, fraud detection, and ML-driven insights

• Developed and implemented advanced features based on user interaction data to enhance user tracking for high-impact recommendation models, driving significant growth and retention.

• Led A/B testing initiatives to improve publish rates, user growth, and re-engagement for target segments.

• Architected algorithms to refine 'For You Page' recommendations, resulting in marked improvements in user engagement and retention.

• Architected and implemented a real-time monitoring solution using Flink to track Mean Scores and Feature Statuses for 400+ models.

• Enhanced the Argo workflow platform by enabling custom sensor arguments and integrating Spark scripts.

• Built a prediction drift monitoring system across 100+ models using Population Stability Index.

• Developed a robust system to monitor and track rolling performance metrics across varying levels of granularity for streamed data in near real-time.

• Engineered an advanced algorithm to monitor AUC in near real-time for streamed production data, leveraging reservoir sampling and probabilistic interpretation of AUC.

• Spearheaded the design and implementation of a caching system using Cassandra for serving model predictions, including A/B experiments to evaluate performance improvements.

• Optimized memory usage by 50% for unmarshalling packed repeated variable-length integers in Gogoprotobuf library used across the company & in open-source projects like Kubernetes & etcd.

Homebrew is a package manager for OSX.

• Led improvements to the test suite for Homebrew using RSpec

• Increased test coverage to 100% for critical components, including git, svn, and analytics utilities, ensuring comprehensive validation and reliability.

• Resolved technical debt and corrected faulty code, optimizing the overall quality and performance of the Brew package management system.