Applied Scientist, Forecasting

About the role Design and evaluate statistical, econometric, and machine learning methods for forecasting problems. Your work will play a central role in increasing forecast accuracy and accelerating delivery timelines Build backtesting and validation frameworks to assess forecast accuracy, stability, and downstream forecast impact. 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. Quantify uncertainty and clearly communicate model confidence, limitations, and trade-offs to technical and non-technical audiences. 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. This role has been categorized as a Remote position. Remote employees do not have a permanent corporate office workplace and, instead, work from a physical location of their choice, which must be identified to the Company. U.S. employees may live in any of the 50 United States, with limited exceptions. In addition to a competitive base pay, employees in this role are eligible for incentive compensation subject to applicable laws and relevant Zillow policies. Actual amounts will vary depending on experience, performance and location. Who you are Advanced degree (PhD or Masters) in Economics, Statistics, Operations Research, Data Science, Computer Science, Econometrics, Mathematics, or a related quantitative discipline or a related quantitative discipline with 3+ years of experiences in professional applied scientist roles Strong experience with time-series forecasting, nowcasting, econometrics, or related quantitative modeling techniques. Strength in data engineering principles to help envision efficient data solutions at scale. Experience explaining complex models and analytical concepts to stakeholders with non-technical backgrounds using clear takeaways and practical business framing. Experience building applied statistical or machine learning models that support real business decisions in production settings. Proficient in Python and SQL for data analysis, model development, validation, and deployment. Comfortable working with incomplete, delayed, or noisy real-world data and designing robust estimation strategies. Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.