Analyze large, complex datasets to understand buyer and seller behavior and uncover the drivers of key real estate and business outcomes.
Develop and deploy time‑series and econometric forecasting models that address critical customer, housing market, and business questions, without exposing proprietary product details.
Own the end‑to‑end modeling lifecycle, including scoping, feature engineering, model development, experimentation, deployment, monitoring, and model explainability.
Translate forecasts into clear insights and recommendations for senior leadership, helping stakeholders understand drivers, uncertainty, and trade‑offs that guide Zillow’s strategy.
Partner cross‑functionally with finance, product, engineering, marketing, and operations to scale and improve forecasting capabilities across Zillow.
Improve and contribute to shared forecasting tools, data pipelines, and processes used across the company.
Collaborate with other applied scientists and data scientists to develop novel solutions to real estate and business problems.
Requirements
Strong experience with quantitative time‑series forecasting techniques and a practical understanding of trade‑offs between performance, explainability, and scalability.
Approximately 7+ years of applied data science experience, including substantial hands‑on work in forecasting, predictive modeling, or econometric analysis (industry or equivalent research experience).
Advanced degree (MS or PhD) in a quantitative field (e.g., Economics, Operations Research, Data Analytics, Statistics, Computer Science, Mathematics, Information Management, Engineering, or related quantitative discipline), or equivalent practical experience.
Proficient in Python and SQL for building, evaluating, and deploying models.
Demonstrated experience monitoring, debugging, and maintaining models in production environments.
Experience explaining complex models and analytical concepts to stakeholders with varied technical backgrounds, using clear takeaways, succinct insights, and effective visualizations.
Tech Stack
Python
SQL
Benefits
competitive base salary
equity awards based on factors such as experience, performance and location