Speaker: David Lundgren

Data Scientist @Opendoor

David is a data scientist on the pricing and revenue optimization group at Opendoor in San Francisco. The team focuses on developing per-home liquidity estimation and building responsive pricing models.

Prior to joining Opendoor, David was a data scientist at Rdio working on music recommenders. He holds a BS in computer science from Binghamton University, and an MS in computer science from the University of Illinois.

Find David Lundgren at

2019 Tracks

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.

  • Handling Sequential Data Like an Expert / ML Applied to Operations

    Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.

    Half-day tracks