Multithread effectively and personalize outreach to convert deals faster
Elevate social presence and drive business growth from social media
Identify and prioritize high-intent leads, and improve sales effectiveness
Find and connect with ICP attendees, and improve event outcomes
Rene Candia is a distinguished Principal Applied Scientist at AWS AI Labs, with a strong background in machine learning, recommender systems, causal inference, and large-scale data analysis. He holds a Ph.D. in Physics from Caltech. Prior to his role at AWS, Rene spent over eight years at Netflix, where he was instrumental in developing and improving their world-class personalization and recommendation algorithms. His expertise lies in the intersection of advanced statistical modeling, machine learning techniques, and their practical application to solve complex, real-world problems at scale, ultimately enhancing user experience and driving business value. He is passionate about bridging the gap between cutting-edge research and impactful product development.
Rene Candia's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Played a key role in advancing Netflix's personalization and recommendation engines, significantly impacting content discovery and user engagement for millions of subscribers worldwide.
Currently leading and contributing to cutting-edge projects in machine learning and causal inference at AWS AI Labs, focusing on developing scalable and impactful AI solutions for cloud customers.
Authored and co-authored numerous influential research papers in physics, machine learning, and data science, published in peer-reviewed journals and presented at prestigious international conferences.
Possesses extensive experience in designing, building, and deploying machine learning models and systems that operate effectively and reliably at massive scale in production environments.
Recognized for applying and advancing causal inference methodologies to understand and improve complex systems, particularly in the context of recommender systems and online platforms.
Harvard Business School - Year 2015
Universidad Adolfo Ibáñez - Year 2010
Universidad Adolfo Ibáñez - Year 2006
Highperformr Signals uncover buying intent and give you clear insights to target the right people at the right time — helping your sales, marketing, and GTM teams close more deals, faster.
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