Applied Scientist

About Opendoor

At Opendoor our mission is to tilt the world in favor of homeowners and those who aim to become one. Home ownership 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 home ownership 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.

About the Role
We’re looking for an Applied Scientist to work on some of the hardest quantitative problems s 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

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