below marketmost similar roles pay hereabove market
This role pays more than
79%
of similar roles. Most pay
$170,962–$241,789
— the blue band above.
At the midpoint, this role pays about
$246k
versus about
$206k
for comparable roles.
Based on 240 similar postings.
Employer
About Opendoor
Opendoor is a digital real estate marketplace that buys and sells homes directly to consumers, simplifying the home selling and buying experience through instant offers and transparent pricing. Industry: Real Estate Technology & iBuying
Opendoor currently has
21 open roles
on FindRole.
Listed pay typically runs
$156,800–$335,000
across 6 roles with salary data.
JOB TITLE: Applied Scientist- Pricing
The Applied Scientist- Pricing joins a small, nimble team to solve complex quantitative problems within a low-margin, high-stakes business. This role focuses on structural modeling, econometrics, optimization, and decision-making under uncertainty to influence pricing, resale strategy, demand modeling, and risk management. You will build models for post-listing demand, estimate price elasticity, and develop optimizers that balance margin, conversion, and risk. Key responsibilities include designing experiments, translating ambiguous business problems into rigorous modeling approaches, and moving prototypes into production-quality scientific code. Required skills include Python, causal inference, Bayesian modeling, and mathematical optimization. You will work with high-dimensional real-world data to create systems for a real estate marketplace, potentially utilizing Pyspark, machine learning, and large language models to improve portfolio risk and inventory decisions.
What does a Applied Scientist earn?
Median $208800 from 61 postings across 10 companies.
Build models to support pricing, resale strategy, and portfolio risk management.
Develop demand and conversion models using pre-listing and post-listing signals.
Design and improve optimization frameworks to balance margin, conversion, and risk.
Apply econometric and mathematical modeling techniques to problems requiring structural understanding.
Design experiments to quantify price elasticity and customer response.
Translate ambiguous business problems into rigorous modeling approaches using high-dimensional data.
Implement production-quality scientific code in Python to turn models into operational systems.
What we're looking for
Experience developing quantitative models to support real-world decision-making under uncertainty.
Strong coding skills in Python to implement production-quality scientific code.
Experience with causal inference, Bayesian modeling, structural modeling, demand forecasting, pricing science, or mathematical optimization.
Ability to translate ambiguous business problems into rigorous modeling approaches using messy, high-dimensional real-world data.
Advanced degree (MS or PhD preferred) in statistics, mathematics, economics, operations research, computer science, or another quantitative discipline.
Strong communication and collaboration skills to work with cross-functional stakeholders and communicate technical ideas clearly.
Experience in pricing, marketplace modeling, revenue management, supply/demand systems, inventory optimization, or risk modeling (preferred).
Background in real estate, housing, finance, or adjacent marketplace domains (preferred).