Applied Scientist

Opendoor; Toronto; On-site; CAD 120,000 - 180,000; Full time

Benefits offered by this job

Unlimited PTO

Medical/dental/vision insurance

Life insurance

401(k)

Job summary

Opendoor in Toronto is seeking an Applied Scientist to tackle hard quantitative problems across pricing, resale strategy, demand modeling, and risk management. This role emphasizes structural modeling, econometrics, and decision‑making under uncertainty, with strong engineering to deploy practical solutions in a fast, low‑margin business.

You will translate messy real‑world data into production‑quality models, design experiments to measure price elasticity, and collaborate with cross‑functional

Qualifications

• 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 causal inference, Bayesian modeling, structural modeling, demand forecasting, pricing science, or mathematical optimization.

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.
• Bring a pragmatic, hands‑on approach: move quickly from idea to prototype to production‑ready scientific component.

Skills

Quantitative modeling

Python

Causal inference

Bayesian modeling

Structural modeling

Demand forecasting

Pricing science

Mathematical optimization

Communication

Education

MS or PhD in a quantitative field

Tools

PySpark

Job description

About the Role

We’re looking for an Applied Scientistto 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.

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)

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

Compensation

Payfor this job range varies by work locationand 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.

At Opendoor our mission is to tilt the world in favor of homeowners and those who aim to become one. Homeownership matters. It’s how people build wealth, stability, and community. It’s how families put down roots, how neighborhoods strengthen, how the future gets built. We’re building the modern system of homeownership giving people the freedom to buy and sell on their own terms. We’ve built an end‑to‑end online experience that has already helped thousands of people and we’re just getting started.