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.
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Led A/B testing initiatives to improve publish rates, user growth, and re-engagement for target segments.
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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.
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Enhanced the Argo workflow platform by enabling custom sensor arguments and integrating Spark scripts.
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Built a prediction drift monitoring system across 100+ models using Population Stability Index.
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Developed a robust system to monitor and track rolling performance metrics across varying levels of granularity for streamed data in near real-time.
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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.
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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.
Led improvements to the test suite for Homebrew using RSpec
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Increased test coverage to 100% for critical components, including git, svn, and analytics utilities, ensuring comprehensive validation and reliability.
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Resolved technical debt and corrected faulty code, optimizing the overall quality and performance of the Brew package management system.
