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About the Role
We are seeking an Applied Scientist to tackle complex quantitative challenges at Opendoor, focusing on machine learning, causal inference, optimization, and decision-making under uncertainty. This role will significantly contribute to our growth ecosystem by developing practical solutions for a high-stakes business, where even minor improvements in acquisition efficiency and customer conversion can yield substantial results. You will work on critical problems such as predicting seller intent and conversion, estimating customer lifetime value, building marketing mix models, and creating optimizers for spend allocation and identifying growth drivers. As part of a small, agile team, you will have the opportunity to influence both modeling strategies and the implementation of these systems in production decision-making.
Responsibilities
- 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.
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 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.
Skills
- Machine learning
- Causal inference
- Optimization
- Decision-making under uncertainty
- Classification
- Statistical modeling
- Experimental design
- Media targeting and measurement
- Marketing mix modeling (MMM)
- Multi-touch attribution (MTA)
- Budget allocation
- Customer acquisition
- Lifecycle marketing
- Personalization
- Customer lifetime value
- Next-best-action systems
- Distributed data processing
- PySpark
Education Level
- Advanced degree (MS or PhD preferred) in statistics, economics, computer science, mathematics, operations research, or a related quantitative field, or equivalent applied research experience.