Opendoor’s Ian Wong on Disrupting the Real Estate Industry with Data-Driven Digital Transformation

Key Takeaways:

• Trust in the numbers: All great algorithms start with great data, but having a high fidelity of data is one of the key differentiators for any high-performing model. When you’re mixing first-party data with third-party data, be intentional about how you create strategic data models that fit your business.
• Data scientists need to hone business skills: As a data professional, it’s not enough to have a breadth of technical skills, coding, algorithms, statistics, and mathematics -- you must also have a firm grasp of business needs with solid communication skills. Remember: your research is not helpful if it does not meet the immediate needs of the business. Being able to find that balance is an integral skill for any young data scientist looking to break into the field.
• Fail fast and experiment When it comes to machine learning, there's a lot of opportunity for failure. Launching a prototype quickly and iterating as you go is the name of the game. It shouldn’t take a quarter to make and deploy a new algorithm. The more time between inception and deployment, the less likely you will be able to use the insights gathered. Stay agile, move quickly, and follow the data.

Key Quotes:

“How do we manage this business, and how do we make sure that we deliver on our product premise, where folks are getting competitive offers and get to enjoy the seamless experience? It has to be backed by data, and it has to be backed by applying the art of machine learning.

The nice thing about using rigorous machine learning, and statistics, is that the data doesn't lie. You can really understand if you're going for an accurate offer, or an accurate valuation the data has to keep me honest. So you're constantly trying to improve, iterate, and reduce the errors so that we can give better and better offers.

Talent, engineering, and technology has always been very difficult to hire. I wouldn't say that it's been easy, but one thing that's really worthwhile for us is that number one, we're trying to really innovate in an antiquated industry. From a mission standpoint that really resonates with a lot of potential employees. They want to make their mark and work on something that's groundbreaking. The harder or more ambitious the problem is, the easier it is to attract great talent.

The algorithms, and we have a whole host of them, have run the business in a very real meaningful way...All great algorithms, start with great data. If you have garbage in you have garbage out, having really high fidelity data is one of the key differentiators for any high-performing model.

Being a data scientist, or data professional is hard. You have to combine technical skills, coding algorithms, mathematics, statistics, you name it. And you have to combine that with commercial instincts.

Bio:

Ian Wong is the co-founder and Chief Technology Officer of Opendoor, where he is responsible for the development of product and technology. Ian is building a team of engineers, data scientists, product managers and designers to modernize the real estate industry. He was previously pursuing his PhD in electrical engineering at Stanford when he left to join Square as their first data scientist. At Square, Ian developed tools and algorithms to handle risk. He has earned Masters degrees in electrical engineering and statistics from Stanford University. As a mission-driven real estate marketplace that radically simplifies home buying and selling, Opendoor has been used by over 85,000 customers in more than 25 metros nationwide.