Data Scientist

About Opendoor

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.

Data Scientist

Location: Hyrbrid 4 onsite, 1 remote 

Opendoor is transforming residential real estate, one of the largest and most complex markets in the world, using data at massive scale. Data science is central to how we build products, understand our customers, guide investment, manage risk, and make decisions across the business. The work is highly leveraged: better measurement, experiments, and insights can improve the customer experience while materially affecting growth and financial performance.

We're looking for an experienced Data Scientist to tackle important, ambiguous problems across marketing, pricing and product. You'll combine statistical reasoning, experimentation, causal inference, and business judgment to shape strategy and turn data into measurable impact. Your work may range from defining metrics and designing experiments to evaluating opportunities and guiding high-stakes business decisions.

You'll be anchored to a team and problem area based on business need and your expertise. Across domains, our data scientists share a common foundation: they partner closely with cross-functional teams, define success, guide roadmaps, and choose the right analytical approach for the decision at hand.

• Frame ambiguous business questions as clear analytical problems with measurable outcomes.
• Design metrics, dashboards, and decision frameworks that help teams understand performance, evaluate tradeoffs, and identify opportunities.
• Design and analyze experiments; apply causal inference and other rigorous methods when randomized tests aren't practical.
• Use statistical analysis, forecasting, and other quantitative methods to evaluate opportunities and inform decisions.
• Own work end to end, from data discovery and validation through analysis, recommendation, and impact measurement.
• Partner with Product, Engineering, Marketing, Operations, and business leaders to shape roadmaps and translate insights into action.
• Communicate findings, uncertainty, tradeoffs, and recommendations clearly to technical and non-technical audiences, including senior leaders.

• Deep statistical reasoning, including experimental design, hypothesis testing, causal inference, and the ability to distinguish meaningful signal from noise.
• Strong SQL and Python skills, with experience working with large, complex, real-world datasets.
• Familiarity with production data pipelines, distributed computing, and modern analytics tooling.
• Deep expertise in one or more areas such as experimentation, causal inference, statistical analysis, forecasting, or optimization.
• Demonstrated ability to structure open-ended problems, make sound methodological tradeoffs, and maintain a high bar for data quality and validation.
• A track record of translating rigorous analysis into clear recommendations and measurable business or customer impact.
• Strong communication and collaboration skills, with the ability to influence partners across functions and levels of technical depth.
• Experience as a data scientist, product analyst, applied scientist, economist, or in a similar quantitative role.

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