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.
We’re looking for an Applied Scientist to work on some of the hardest quantitative problems at Opendoor. This role will focus primarily on machine learning, causal inference, optimization, and decision-making under uncertainty, with applications spanning marketing investment, customer acquisition, lifecycle engagement, and conversion.
This role will contribute to our broader growth 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 in acquisition efficiency and customer conversion can have an outsized impact.
You’ll work on problems like predicting seller intent and conversion, estimating customer lifetime value, building marketing mix models, and developing optimizers that help us allocate spend and identify which customer interactions drive incremental growth.
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- Strong Python skills, with experience building maintainable software and contributing to production ML systems.
- Experience taking predictive models from problem definition and training through deployment, evaluation, and iteration, with a strong foundation in classification and statistical modeling.
- Applied experience in causal inference and experimental design, including estimating incremental effects and reasoning about confounding, selection bias, and uncertainty.
- Ability to evaluate models using both predictive performance and the business outcomes of the decisions they inform.
- Comfort working with imperfect data, delayed outcomes, and ambiguous business questions, and translating findings into clear recommendations for technical and business partners.
- An advanced degree (MS or PhD preferred) in statistics, economics, computer science, mathematics, operations research, or a related quantitative field, or equivalent applied research experience.
- Experience in media targeting and measurement, including marketing mix modeling (MMM), multi-touch attribution (MTA), and budget allocation.
- Experience in customer acquisition, lifecycle marketing, and personalization, including customer lifetime value and next-best-action systems.
- Background in real estate, housing, and other marketplaces with long customer decision cycles.
- Familiarity with distributed data processing, such as PySpark.
- Build models and decision systems that support profitable growth through better customer acquisition and engagement.
- Develop intent, conversion, and lifetime value models to optimize acquisition and customer engagement.
- Build and improve marketing measurement and optimization systems that guide budgeting and the allocation of marketing spend across channels, markets, and time.
- Apply causal inference to experimental and observational data to estimate incremental marketing impact and how it varies across customers and markets.
- Partner with Marketing, Product, Engineering, and Sales to turn models into systems that influence real decisions.
- Bring a pragmatic, hands-on approach: move quickly from research and prototyping to production, and own ongoing model evaluation and improvement.
