Zillow pioneered providing access to unprecedented information about the housing market. Long gone are the days when you needed an agent to get comparables and prior sale and listing data. Enter Zillow, the nation’s number-one real estate website and mobile app. With more data, data science has enabled more use cases. Jasjeet Thind explores Zillow’s big data platform, discusses some of its core machine-learning algorithms, and outlines best practices for scaling streaming data ingestion and data processing in Spark.
Jasjeet Thind is the vice president of data science and engineering at Zillow. His group focuses on machine-learned prediction models and big data systems that power use cases such as Zestimates, personalization, housing indices, search, content recommendations, and user segmentation. Prior to Zillow, Jasjeet served as director of engineering at Yahoo, where he architected a machine-learned real-time big data platform leveraging social signals for user interest signals and content prediction. The system powers personalized content on Yahoo, Yahoo Sports, and Yahoo News. Jasjeet holds a BS and master’s degree in computer science from Cornell University.
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