Teams at Scribd, Inc.

Lead the recommendations product across candidate generation, ranking, and presentation for Scribd. Define multi-year strategy and metrics, translate retrieval and ranking research into production, run experiments to improve engagement and conversion, partner with ML, engineering, design, and analytics, and communicate roadmap and impact to stakeholders.

Lead and grow a Data Engineering team to build production data pipelines, medallion datasets, and reusable data products. Drive architecture, engineering standards, design reviews, and cross-functional delivery. Partner with Product, Analytics, Data Science, and Platform to enable governed, reliable data foundations for analytics and AI. Mentor engineers and manage multiple concurrent initiatives to deliver predictable outcomes.

Lead strategy and execution for Scribd's Search product, defining multi-year roadmaps and metrics, partnering with ML, engineering, design, and analytics to ship ML-driven retrieval and ranking features that improve relevance, engagement, and conversion across large-scale consumer surfaces.