Opendoor is transforming the residential real estate market by using data at scale to price homes, manage risk, and improve capital efficiency. The quality of pricing decisions directly impacts conversion, margins, customer trust, and financial performance.
Requirements
- Deep statistical reasoning: hypothesis design, experimental design, causal inference, and ability to distinguish signal vs noise.
- Proven end-to-end ML ownership: data acquisition, feature engineering, model development, validation, deployment, and ongoing monitoring.
- Strong SQL + Python proficiency; comfortable working with production data pipelines and modern ML tooling (e.g., Spark, Airflow, Ray, SageMaker, Vertex, etc.).
- Demonstrated ability to translate complex analytical findings into clear business recommendations and influence cross-functional decision-making.
- Experience working with ill-defined problems and driving clarity on problem definition, success metrics, and realistic tradeoffs.
- High data-quality bar: disciplined approach to validation, bias analysis, and making decisions rooted in evidence vs intuition.
- Effective communicator — able to tell the story behind the model to both highly technical and non-technical audiences.
Responsibilities
- Build and maintain pricing metrics, dashboards, and frameworks.
- Run experiments and causal analyses to measure impact and drive decisions.
- Develop predictive + statistical models that improve pricing accuracy.
- Partner closely with Product, Engineering, and Operations teams to influence roadmap and model deployment.
- Deliver insights and narratives that inform executive strategy.
Other
- In office 4 days a week
- Mid to senior level Data Scientists
- Operate at the intersection of economics, machine learning, experimentation, and product strategy
- Tackle ambiguity, shape the pricing roadmap, and build models/analyses that materially move the business
- Influence how we evaluate millions of dollars of housing inventory
