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Opendoor is building the modern system of homeownership, enabling people to buy and sell homes on their own terms through an end-to-end online platform. The company's mission is to tilt the world in favor of homeowners and those aspiring to become one.
You will join a small, nimble growth team as an Applied Scientist focused on machine learning, causal inference, optimization, and decision-making under uncertainty. Your work will span marketing investment, customer acquisition, lifecycle engagement, and conversion—areas where small improvements in efficiency and conversion can have outsized impact in a low-margin, high-stakes business.
Key problems you'll tackle include predicting seller intent and conversion, estimating customer lifetime value, building marketing mix models, and developing optimizers that allocate spend and identify which customer interactions drive incremental growth. You'll combine strong modeling intuition with hands-on execution and engineering to build practical, production-ready solutions.
You will build models and decision systems that support profitable growth; develop intent, conversion, and lifetime value models to optimize acquisition and engagement; build and improve marketing measurement and optimization systems that guide budgeting and spend allocation across channels, markets, and time; apply causal inference to experimental and observational data to estimate incremental marketing impact; partner with Marketing, Product, Engineering, and Sales to turn models into systems that influence real decisions; and bring a pragmatic, hands-on approach—moving quickly from research and prototyping to production while owning ongoing model evaluation and improvement.
REQUIREMENTS:
- 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 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; ability to translate findings into clear recommendations for technical and business partners
- Advanced degree (MS or PhD preferred) in statistics, economics, computer science, mathematics, operations research, or related quantitative field, or equivalent applied research experience
NICE TO HAVE:
- 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
