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 Senior Risk Researcher, you will be instrumental in enhancing Opendoor's pricing and risk management frameworks through the application of sophisticated mathematical and computational techniques. The ideal candidate should possess a background in operations research and financial engineering with some knowledge of convex optimization, optimal control, and python programming. If you have a passion for applying quantitative finance to real-world problems, we would love to hear from you.

Role Responsibilities:

Develop, document, and implement models for pricing and risk analysis in the real estate market, focusing on optimizing bid/ask spreads to manage the trade off between income and inventory risk over time
Apply expertise in stochastic processes and time series methods to model housing market volume and price processes to account for seasonality, drift, volatilities, and correlations
Utilize knowledge of convex optimization / stochastic optimal control to improve our pricing strategies while effectively managing inventory risks
Collaborate with cross-functional teams to integrate these models into our pricing, risk management, and finance systems, ensuring they align with our business objectives and market dynamics
Stay abreast of the latest research and methodologies and continually refine and advance our risk management strategies

Skills Needed:

PhD in Mathematical Finance, Operations Research, or a related field, with knowledge of stochastic processes, convex optimization, and optimal control
Proven experience in developing Python-based algorithms for complex financial models
Strong understanding of real estate markets, transaction volumes, market liquidity, home price dynamics, and inventory risk
Ability to translate theoretical models into practical, actionable strategies
Excellent problem-solving skills and the ability to work in a fast-paced, evolving environment

Bonus Points if:

CFA Designation
GARP FRM Accreditation
Professional experience with CVXPy

Location:

Remote roles in the US are available in all states EXCEPT Hawaii, Alaska, Montana, or any US Territories.

Compensation:

Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The pay range for most locations in the US is $176,800 - $297,600 annually. In the SF Bay Area of California, Seattle, and New York City Metro area the base salary range is $220,800 - $331,200. Pay within the range varies by work location and may also depend on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process. We also offer a comprehensive package of benefits including paid time off, 12 paid holidays per year, medical/dental/vision insurance, basic life insurance, and 401(k) to eligible employees.

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