Applied Scientist

The Housing Trends Metrics and Forecasting team builds and maintains the data pipelines, metric definitions, and analytical methods behind Zillow’s housing market reporting, internal business metrics, forecasting, and market intelligence products. The team works across listing, transaction, and property-attribute data to produce recurring housing metrics that provide timely insight into real estate market trends and inform business forecasting, financial analysis, economic research, and sales and marketing operations. Zillow Group is a strategic, mission-driven organization focused on delivering exceptional experiences and measurable outcomes. Our work spans cross-functional partnership, scalable programs and operational excellence in support of Zillow’s mission. We bring deep experience working across diverse teams in a dynamic, high-growth environment, balancing strategic thinking with hands-on execution to drive meaningful business impact. We are seeking an experienced professional to support our workforce expansion in India.

Zillow is looking for an Applied Scientist to join Housing Trends Metrics and Forecasting. In this role, you will own scoped, high-impact work that improves the reliability, quality, and maintainability of Zillow’s published housing metrics. You will support recurring metric publication, investigate metric anomalies and possible data outages, strengthen data quality checks, and partner closely with engineers on pipeline migrations and system improvements. This role is well suited for someone who is comfortable moving between analytical investigation and production data work. You should be able to use SQL, Python, and Spark to debug issues, validate upstream changes, improve metric logic, and build durable solutions for live metric systems. You should also be able to take ambiguous measurement or data quality problems, break them into manageable pieces, and clearly explain both the technical trade-offs and business impact of your recommendations.   You Will Get To Support recurring publication of housing metrics by monitoring outputs, validating changes, and resolving issues before they affect downstream consumers. Investigate metric anomalies, suspected bugs, and possible data outages by tracing issues across source data, transformation logic, and publication workflows. Design and implement stronger data quality checks, validation workflows, and monitoring patterns for production metric pipelines. Partner with engineers to migrate pipelines, reduce fragile upstream dependencies, and improve system reliability and maintainability. Evaluate the impact of upstream schema, logic, or source-data changes against historical baselines and communicate revisions, caveats, and trade-offs to stakeholders. Improve metric definitions, documentation, and operational workflows so recurring processes are more reproducible, explainable, and resilient. This role has been categorized as an Office position. “Office” employees regularly work at the Zillow India office for approximately 80 to 100 percent of their time each month. Employees must live within a reasonable commuting distance of the office. Zillow has not defined a reasonable distance, and expects employees will use judgment in determining this for themselves and understand the implications re: time commitment and cost of daily commute. 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.

You can independently own a well-scoped problem and drive it from investigation through validated recommendation or implementation. You know how to translate a business or measurement question into a data plan, implement the analysis, test the output, and clearly explain the result. You are comfortable working with large operational datasets and