Senior Applied Scientist

Zillow is seeking an Applied Scientist based in India to focus full-time on developing and improving forecasting performance for Zillow's business lines.

In this role, you will design, implement, and refine advanced statistical and machine learning approaches to generate accurate, scalable forecasts. You will partner with team members to expedite and improve the quality of modeling work. Further, you will collaborate closely with cross-functional teams to translate complex data into actionable insights.

Responsibilities:

• 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.
• Propose and evaluate novel methods to reasonably forecast asset-level performance.
• 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.

Requirements:

• Advanced degree (PhD or master's) in economics, statistics, operations research, data science, computer science, econometrics, mathematics, or a related quantitative discipline or a related quantitative discipline with 5+ years of experience in professional applied scientist roles
• Strong experience with time-series forecasting, 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.