Applied Scientist

About the Role

Opendoor is seeking an Applied Scientist to tackle some of the hardest quantitative problems on our pricing, resale strategy, and risk management platforms. This role focuses on structural modeling, econometrics, optimization, and decision-making under uncertainty, with applications spanning pricing, resale strategy, demand modeling, and portfolio risk. You’ll contribute to our valuation and pricing ecosystem and build practical, production-ready solutions for a low-margin, high-stakes business.

What You'll Do

• Build models to support pricing, resale strategy, demand modeling, and portfolio risk across our products and inventory

• Design and run experiments to quantify price elasticity and customer response; develop pre- and post-listing demand/conversion models

• Develop, test, and deploy optimization frameworks balancing objectives such as margin, conversion, and risk

• Apply statistical, econometric, and mathematical modeling techniques where structure matters; translate ambiguous business problems into rigorous modeling approaches

• Work with messy, high-dimensional real-world data and move quickly from idea to prototype to production-ready code

• Partner with Engineering, Product, and Operations to turn models into production systems used in real decisions

• Communicate technical ideas clearly to cross-functional stakeholders

• What We're Looking For

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

• Strong Python coding skills; ability to move beyond prototyping to production-quality scientific code

• Experience with one or more of: causal inference, Bayesian modeling, structural modeling, demand forecasting, pricing science, or mathematical optimization

• Comfortable with messy data and translating 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; comfortable working with cross-functional stakeholders

• 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)

• Nice to Have

• Familiarity with distributed data processing tools such as PySpark (nice to have)

• Ability to shape modeling direction and production deployment in a fast-paced environment

• Compensation & Benefits

• The base pay range for this position is $156,800-$335,000 annually, plus RSUs. Pay within this range varies by work location and may also depend on qualifications, knowledge, skills, and experience

• Benefits include unlimited PTO, medical/dental/vision insurance, life insurance, and 401(k) eligibility