Principal Data Scientist - Rentals Shopping

About the team

Zillow Group’s mission is to give people the power to unlock life's next chapter. We are seeking a Principal Data Scientist to serve as the data science lead for the Rentals Shopping pillar, the team that improves how millions of renters discover and secure their next home through Zillow’s marketplace.

This team sits at the heart of the rental consumer journey and focuses on advancing our product and consumer experiences through rigorous analysis, experimentation, and proactive discovery of business opportunities across the rental shopping funnel (from search to contact and conversion). As the Shopping pillar lead, you will be a key strategic partner to product leadership, helping shape the roadmap and ensuring decisions are grounded in high‑quality data science in a highly competitive market.

About the role

As a Principal Data Scientist, you will discover and lead large‑scale, data‑driven opportunities that help define the future of the Rentals Shopping pillar. You will be the primary data science partner for product leadership, co‑developing the data strategy and ensuring that data science is a core driver of the cross‑functional roadmap. Your work will directly influence how renters search, evaluate, and connect with rental homes, and how we grow Zillow’s Rentals business.

You Will Get To

• Drive the long‑term data strategy for the Rentals Shopping pillar, using deep analysis of consumer behavior and market dynamics to identify high‑impact growth opportunities and inform the product roadmap.

• Elevate statistical rigor, modeling, and experimentation practices across Rentals to enable high‑confidence decision‑making.

• Partner with product, engineering, and design leaders to translate complex analysis into clear, actionable product and business strategies.

• Lead analytical and modeling work across critical problem spaces such as rental search relevance, ranking, personalization, or marketplace efficiency.

• Design and guide robust A/B tests and other experimentation frameworks to measure the impact of new features and optimizations in the rental shopping experience.

• Apply modern AI (including LLMs and agentic workflows) to increase data science velocity, improve model quality, and scale intelligent user-facing solutions across the Rentals marketplace.

• Coach and mentor data scientists, providing technical leadership and helping to unblock complex analytical and modeling challenges.

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 California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington state, and Washington DC the standard base pay range for this role is $178,300.00 - $284,700.00 annually. This base pay range is specific to these locations and may not be applicable to other locations. In Colorado, Hawaii, Illinois, Minnesota, Nevada, Ohio, Rhode Island, and Vermont the standard base pay range for this role is $169,300.00 - $270,500.00 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.

Who you are

You are comfortable leading through ambiguity in a growing marketplace and enjoy making data‑informed decisions in fast‑changing environments. You bring deep technical expertise and strong product intuition, and you know how to influence cross‑functional partners using clear, actionable insights.

• Ph.D. or Master’s degree in a quantitative field (e.g., Statistics, Economics, Computer Science), or equivalent practical experience.

• Substantial experience (typically 8+ years) in quantitative analysis, advanced modeling, and experimentation at scale in consumer‑facing technology environments.

• Technical skills: Expert in SQL and Python, with experience applying statistical modeling (e.g., causal inference, time series) in big data environments such as Databricks or Snowflake.

• Product and business impact: Proven track record of shap