Drove the development of user profiling algorithms, experimenting with advanced clustering techniques and zero-shot classification using Facebook’s BART to map over traveler’s clustered memories to Expedia’s taxonomy, improving personalization.

Optimized the LTM (Long Term Memory) model with Redis-based data storage, enhancing user profile refinement and ensuring seamless integration with the core taxonomy, reducing average system latency by 35%.

Designed and deployed a robust ensemble-based model on Flyte and Databricks, parameterized for scalability, validated with a Pytest suite and cluster silhouette scores of 0.6–0.7, ensuring high model reliability and performance.

Collaborated with Senior Director of Machine Learning Science to align ML models with business objectives, stakeholder requirements, and measurable outcomes.

Increased customer engagement by 20% through strategic KPI analysis by designing a customer segmentation model using clustering algorithms to identify high-value cohorts and tailored engagement strategies.

Saved 480 man-hours in just one quarter by deploying and optimizing Tableau dashboards and data pipelines using Python, SQL, Dataiku DSS and Alteryx across EMEA, APAC, AMER markets.

Boosted customer satisfaction scores by 22% by conducting rigorous SIT, DQM checks, and QA/QC processes over 300 KPIs derived from customer engagement data.