Applied Scientist- Pricing

About the role

We are seeking an Applied Scientist to join our team. This role will focus on quantitative modeling, econometrics, optimization, and decision-making under uncertainty, with applications in pricing, resale strategy, demand modeling, and risk management. The ideal candidate will contribute to our valuation and pricing ecosystem and will be responsible for combining strong modeling intuition with practical implementation.

Responsibilities

• 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

Requirements

• 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

Qualifications

• Nice to have: 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)

Skills

• Experience in applied science and quantitative modeling
• Strong coding skills in Python
• Knowledge of statistical, econometric, and mathematical modeling techniques
• Ability to work with complex, real-world data
• Effective communication and collaboration skills

Benefits

We offer a competitive compensation package, including a base pay range of $156,800-$335,000 annually, plus RSUs. Additional benefits include unlimited PTO, medical/dental/vision insurance, life insurance, and a 401(k) plan for eligible employees.