Senior Data Scientist

About the team

Zillow Group’s Business Data organization represents the next step forward in the company's dedication to integrate an improved set of B2B agent software & advertising products for customers and partners, their clients, and the real estate industry as a whole. Zillow has built and acquired a portfolio of market-leading products for agent productivity, listing media, showing coordination, transaction management and analytics solutions.

Our wide array of products and services are built on technological innovations crafted to bring efficiencies to all users.

The Partner Analytics team within Business Data is hiring a Senior Data Scientist to lead analytics to uncover insights to drive both better business decisions and customer experiences across our product portfolio.

• Independently scope and deliver end-to-end data science projects, from problem framing through model development, validation, and productionization
• Build and maintain industry performance and customer segmentation models, applying appropriate techniques (e.g., clustering, regression, tree-based models) and ensuring they are robust, interpretable, and actionable
• Partner with Data Engineering and ML Engineering to deploy and maintain models in production, including feature development, batch/real-time scoring, and monitoring model performance over time
• Design and analyze experiments and observational studies (A/B testing, causal inference) to evaluate product features and business initiatives
• Translate ambiguous business problems into well-defined analytical approaches, selecting appropriate methodologies and success metrics
• Provide data-driven recommendations to senior leaders, clearly communicating trade-offs, assumptions, and impact
• Collaborate with cross-functional teams including Follow Up Boss, Preferred, and zPro to align on goals, define KPIs, and deliver integrated insights
• Contribute to scalable data assets (clean datasets, metrics definitions, dashboards) that enable self-service analytics
• Mentor junior team members and contribute to team best practices, code quality, and documentation

• Owns moderately complex problem areas with clear business impact, delivering solutions with limited oversight
• Influences decision-making within their immediate domain and contributes to adjacent areas through collaboration
• Balances speed and rigor, making sound trade-offs in modeling, experimentation, and analysis
• Begins shaping team standards and improving how data science work is executed within the org

U.S. employees may live in any of the 50 United States, with limited exceptions. 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.

In California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington state, and Washington DC the standard base pay range for this role is $ - $ annually. This base pay range is specific to these locations and may not be applicable to other locations. In Colorado, Hawaii, Illinois, Maine, Minnesota, Nevada, Ohio, Rhode Island, Vermont, and Virginia the standard base pay range for this role is $ - $ annually.

The base pay range is specific to these locations and may not be applicable to other locations. In addition to a competitive base salary this position is also eligible for equity awards based on factors such as experience, performance and location. Actual amounts will vary depending on experience, performance and location. Employees in this role will not be paid below the salary threshold for exempt employees in the state where they reside.

• 4–7+ years of experience in data science, applied machine learning, or a related field
• Strong foundation in statistics, machine learning, and experimentation, with demonstrated ability to apply methods to real-world business problems
• Proficient in Python (pandas, scikit-learn, PySpark) and SQL; comfortable working with large datasets in modern data platforms (e.g., Snowflake, Spark)
• Experience building…