Founded in 2014, Opendoor’s mission is to empower everyone with the freedom to move. We believe the traditional real estate process is broken and our goal is simple: build a digital, end-to-end customer experience that makes buying and selling a home simple, certain and fast. We have assembled a dedicated team with diverse backgrounds to support more than 100,000 homes bought and sold with us and the customers who have selected Opendoor as a trusted partner in handling one of their largest financial transactions. But the work is far from over as we continue to grow in new markets. Transforming the real estate industry takes tenacity and dedication. It takes problem solvers and builders. It takes a tight knit community of teammates doing the best work of their lives, pushing one another to transform a complicated process into a simple one. So where do you fit in? Whether you’re passionate about real estate, people, numbers, words, code, or strategy -- we have a place for you. Real estate is broken. Come help us fix it.
About the Role:
As a Staff Data Scientist, you will lead the efforts to advance Opendoor’s ability to learn causes and effects. As a business focused on a small number of large transactions with a high degree of operational complexity, Opendoor cannot simply use rapid A/B testing as a way to establish cause-effect relationships for our strategic decisions. You will define and lead pragmatic causal learning practice at a company level using advanced experimentation, inference, and decision science techniques. You will identify the limits of our current practices and continually incorporate state-of-the-art academia and industry, potentially drawing from non-obvious domains or applications. Moreover, you will have the technical command, business context, and leadership stature to drive Data Science technical direction, cross-functional decision-making, and a culture of causal reasoning throughout the company.
As a Staff Data Scientist, you will:
- Define and drive day-to-day causal learning practices across Opendoor business
- Drive technical direction on advanced econometrics, causal inference, experimental design, etc. techniques across the Research & Data Science organization
- Bridge cutting-edge academic and industrial research with the underlying shape of our strategic business questions to identify, synthesize and apply relevant insights.
- Shape causal reasoning culture across Engineering, Product, Operations, Design, among other functions
We’re looking for teammates who have:
- Advanced expertise in one of the following domains: causal inference, econometrics, experimental design, clinical trial, or any other fields where you’ve focused on establishing causality with data
- Interests and proficiency in working with fast-evolving data sets around complex business
- Strong strategic thinking and problem-solving skills to apply academic knowledge on practical business questions
- Strong communication and leadership skills to influence non-technical audiences with analytical insights
- 7+ years of industry experience with an advanced degree in a quantitative field
- Programming sufficiency: SQL, Python/R, or any other of your preferred languages
- You are excited to join a radically transparent team
- You are committed to iteratively driving results
- You are propelled forward by working on hard data science problems
- You are interested in how to make the best decisions under uncertainty
More About Us:
Want to learn more about us and how we are revolutionizing the home buying and selling process? Learn more about us on our website, check out our profile on The Muse to learn more about our culture from our team members, or read our blog posts to hear about the work we are doing.
We Offer the Following Benefits and Perks:
- Full medical, dental, and vision with optional 70% coverage for dependents
- Flexible vacation policy
- Generous parental leave
- Paid time off to volunteer
Please note that these benefits and perks are available only to Full Time team members and do not apply to contract roles.
Opendoor Values Openness: