Summary
Designed and implemented cutting-edge recommender system algorithms to enhance user experience and drive key business metrics for a high-traffic digital platform.
Highlights
Developed and deployed a novel deep learning-based recommendation model, increasing user engagement by 18% and content consumption by 12% across core product lines.
Optimized real-time inference pipelines for scalable recommender systems, reducing latency by 25% and improving throughput to serve over 50 million daily recommendations.
Conducted rigorous A/B testing and statistical analysis on new algorithm iterations, leading to the adoption of features that boosted conversion rates by 7% and revenue by 5%.
Engineered impactful features from large-scale user behavior and item metadata, improving model accuracy (e.g., 15% reduction in RMSE) and personalization effectiveness.