Applied Scientist

About the Role

Applied Scientist to work on some of the hardest quantitative problems at Opendoor, focusing 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 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.

What You'll Do

• Build models that inform pricing, resale strategy, demand modeling, and portfolio risk across our products and inventory.

• Design and implement optimization frameworks that balance objectives like margin, conversion, and risk.

• Develop demand and conversion models using both pre-listing and post-listing signals.

• Design experiments and measurement approaches to quantify price elasticity, customer response, and product trade-offs.

• Translate ambiguous business problems into rigorous modeling approaches and move quickly from idea to prototype to production-ready components.

• Partner with Engineering, Product, and Operations to turn models into systems that influence real decisions.

• Communicate technical ideas clearly to cross-functional stakeholders and shape modeling direction in a nimble, small team.

• What We're Looking For

• Advanced degree (MS or PhD) in statistics, mathematics, economics, operations research, computer science, or another quantitative discipline.

• Experience developing quantitative models to support decision-making under uncertainty.

• Strong Python coding skills, 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.

• Experience in pricing, marketplace modeling, revenue management, supply/demand systems, inventory optimization, or risk modeling.

• Background in real estate, housing, finance, or adjacent marketplace domains.

• Familiarity with distributed data processing tools such as PySpark.

• Experience with machine learning methods broadly, including where deep learning can complement structured statistical modeling.

• Experience working with large language models (LLMs) or vision-language models (VLMs).

• Strong communication and collaboration skills — you’re comfortable working with cross-functional stakeholders and can communicate technical ideas clearly.

• Compensation & Benefits

• Salary range: $156,800 - $335,000 annually, plus RSUs.

• Benefits: unlimited PTO, medical/dental/vision insurance, life insurance, and 401(k) to eligible employees.