This role pays more than
75%
of similar roles. Most pay
$162,375–$247,718
— the shaded band above.
At the midpoint, this role pays about
$246k
versus about
$205k
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
31 open roles
on FindRole.
Listed pay typically runs
$156,800–$335,000
across 6 roles with salary data.
As an Applied Scientist- Pricing, you will join a nimble team focused on solving complex quantitative problems within the valuation and pricing ecosystem. You will build structural models, develop demand forecasting tools, and design optimization frameworks to manage risk and improve resale strategy in a high-stakes, low-margin business environment. Your daily work involves modeling post-listing demand, estimating price elasticity, designing experiments, and translating ambiguous business problems into rigorous mathematical approaches. To succeed, you must possess strong coding skills in Python to move from prototype to production-quality code. The role requires expertise in areas such as causal inference, Bayesian modeling, pricing science, or mathematical optimization. You will work with high-dimensional data to influence real decisions while collaborating with cross-functional partners to build systems that balance margin, conversion, and risk using statistical and econometric techniques.
What does a Applied Scientist earn?
Median $208800 from 47 postings across 10 companies.
Build models to inform pricing, resale strategy, and portfolio risk management.
Develop demand and conversion models using pre-listing and post-listing signals.
Design optimization frameworks that balance margin, conversion, and risk objectives.
Apply structural modeling, econometrics, and mathematical techniques to solve complex business problems.
Design experiments to quantify price elasticity and customer response.
Translate ambiguous business problems into rigorous, production-ready scientific models.
Implement high-quality Python code to move models from prototype to production systems.
What we're looking for
Experience developing quantitative models for decision-making under uncertainty.
Proficiency in Python with the ability to implement production-quality scientific code.
Expertise in 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 data.
Advanced degree (MS or PhD preferred) in a quantitative discipline such as statistics, mathematics, economics, operations research, or computer science.
Strong communication and collaboration skills to convey technical ideas to cross-functional stakeholders.
Experience with pricing, marketplace modeling, revenue management, supply/demand systems, inventory optimization, or risk modeling.
Familiarity with distributed data processing tools like PySpark and experience with machine learning or large language models.