Product of Relevance, Airbnb
Daniel is passionate about all things AI, from the math underpinning new techniques to their engineering implementation, and to their tangible (and often transformative) impact on the bottom line. Currently, he spearheads product strategy, road mapping, and implementation for Airbnb’s Relevance team. Daniel coordinates with teams across engineering, design, and
marketing, to create an industry-leading experience driven by the latest in machine learning advances. His responsibilities include working directly with artificial intelligence engineers on cutting edge search, ranking, and recommendation problems, deciding the best way to approach data-driven tradeoffs between a portfolio of business objectives in the search stack, defining, building, and analyzing KPIs/metrics for the success of products. Prior to his current role, Daniel was a Staff Product Manager at Etsy, where he was responsible for vision and roadmap for AI-driven search growth, and led the team responsible for upgrading Etsy’s first-gen machine learning search stack to a multi-tiered system designed to optimally balance multiple business objectives while achieving state-of-the-art search performance. Additionally, he guided the development and deployment of product features that netted >$100m growth in Etsy’s marketplace by increasing conversion, optimizing sales price, and improving customers’ search experience. Prior depth of experience in commercial strategy, Product Management across the innovation lifecycle, and developing AI architecture & code has given Daniel a unique knowledge base and skill set in the design of machine learning products that achieve real, measurable impact.
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