Applied Scientist
Opendoor
Apply to this job Toronto Until 8/21/2026 H-1B sponsor history First posted May 1, 2026 Last posted May 1, 2026
Job description
About the Role
We’re looking for an Applied Scientist to work on some of the hardest quantitative problems at Opendoor. This role will focus primarily 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 broader 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.
You’ll work on problems like modeling post-listing demand, estimating price elasticity, designing experiments, building structural models, and developing optimizers that help us make better decisions across our products and inventory.
We’re a small, nimble team, so there’s ample opportunity to shape both the modeling direction and how these systems get used in production decision-making.
This role will contribute to our broader 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.
You’ll work on problems like modeling post-listing demand, estimating price elasticity, designing experiments, building structural models, and developing optimizers that help us make better decisions across our products and inventory.
We’re a small, nimble team, so there’s ample opportunity to shape both the modeling direction and how these systems get used in production decision-making.
What You'll Need
- 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
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)
• 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)
What You'll Do
• 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
• Bring a pragmatic, hands-on approach: move quickly from idea to prototype to production-ready scientific component
• 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
• Bring a pragmatic, hands-on approach: move quickly from idea to prototype to production-ready scientific component
Compensation
Pay for this job range varies by work location and may also depend on your qualifications, job-related knowledge, skills, and experience. We also offer a comprehensive package of benefits including unlimited PTO, medical/dental/vision insurance, life insurance, and 401(k) to eligible employees.
About this role
Summary
Build models for pricing, demand, risk; develop optimization frameworks; collaborate cross-functionally.
Job title
Applied Scientist
Experience level
MS or PhD preferred
Industry
real estate
Location requirements
Toronto, remote work not mentioned
Salary
Not specified
Visa sponsorship
H-1B sponsor history
Management role
No
Skills & keywords
Required skills
Pythoncausal inferenceBayesian modelingstructural modelingmathematical optimization
Preferred skills
pricingmarketplace modelingdemand managementdistributed data processingmachine learning
Specializations
modelingeconometricsoptimizationdemand forecastingpricing
Locations
Structured locations inferred from the posting.
Toronto, ON, Canada
On-site City
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