Applied Scientist, Opendoor

• Experience developing quantitative models to support real-world decision-making under uncertainty
• Strong coding skills in Python, with the ability to move beyond prototyping and implement production-quality scientific code
• Experience with one or more of the following: causal inference, Bayesian modeling, structural modeling, demand forecasting, pricing science, or mathematical optimization
• Comfort working with messy, high-dimensional real-world data and translating ambiguous business problems into rigorous modeling approaches
• Advanced degree (MS or PhD preferred) in statistics, mathematics, economics, operations research, computer science, or another quantitative discipline
• Strong communication and collaboration skills — you’re comfortable working with cross-functional stakeholders and can communicate technical ideas clearly

Desirable

• We’re looking for an Applied Scientist to work on some of the hardest quantitative problems at Opendoor
• This role will focus primarily on structural modeling, econometrics, optimization, and decision-making under uncertainty, with applications spanning pricing, resale strategy, demand modeling, and risk management
• This role will contribute to our broader valuation and pricing ecosystem and we’re looking for someone who can combine strong modeling intuition with hands-on execution and strong engineering to build practical solutions for a low-margin, high-stakes business where small improvements can have an outsized impact
• You’ll work on problems like modeling post-listing demand, estimating price elasticity, designing experiments, building structural models, and developing optimizers that help us make better decisions across our products and inventory
• We’re a small, nimble team, so there’s ample opportunity to shape both the modeling direction and how these systems get used in production decision-making
• Build models that help Opendoor make better decisions around pricing, resale strategy, and portfolio risk
• Develop demand and conversion models using both pre-listing and post-listing signals
• Design and improve optimization frameworks that balance objectives like margin, conversion, and risk
• Apply statistical, econometric, and mathematical modeling techniques to problems where structure matters and pure black-box prediction is not enough
• Design experiments and measurement approaches to quantify price elasticity, customer response, and product trade-offs
• Partner with Engineering, Product, and Operations to turn models into systems that influence real decisions
• Bring a pragmatic, hands-on approach: move quickly from idea to prototype to production-ready scientific component