SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
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 can have outsized impact in a low-margin, high-stakes business.
Key responsibilities include:
- Build predictive models for seller intent, conversion, and customer lifetime value to optimize acquisition and engagement
- Develop and improve marketing measurement and optimization systems that guide budget allocation across channels, markets, and time
- Apply causal inference to experimental and observational data to estimate incremental marketing impact and heterogeneous effects
- Create marketing mix models (MMM), multi-touch attribution (MTA), and budget allocation systems
- Partner with Marketing, Product, Engineering, and Sales to translate models into production decision systems
- Move quickly from research and prototyping to production, owning ongoing model evaluation and iteration
- Work with imperfect data, delayed outcomes, and ambiguous business questions, translating findings into clear recommendations
You will combine strong modeling intuition with hands-on execution and engineering to build practical solutions that drive growth.
REQUIREMENTS:
- Strong Python skills with experience building maintainable software and contributing to production ML systems
- Experience taking predictive models from problem definition through training, deployment, evaluation, and iteration
- 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 business outcomes of the decisions they inform
- Comfort working with imperfect data, delayed outcomes, and ambiguous business questions
- 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 MMM, MTA, and budget allocation
- Background in customer acquisition, lifecycle marketing, and personalization, including CLV and next-best-action systems
- Experience in real estate, housing, or other marketplaces with long customer decision cycles
- Familiarity with distributed data processing (e.g., PySpark)
