Data Scientist II
ZillowRemote-USA$126k – $201kPosted 30 March 2026
Tech Stack
Job Description
About the team
Zillow Group’s mission is to give people the power to unlock life’s next chapter. The Rentals Data Science – Shopping team sits at the center of the renter journey, partnering with product and engineering to power how millions of renters discover, evaluate, and secure their next home across our marketplaces.
About the role
As a Data Scientist on the Rentals Data Science – Shopping team, you will partner closely with product, engineering, design, and machine learning (ML) to solve high-impact problems in the rental shopping experience. You will use data to shape product strategy, design and analyze experiments, and uncover opportunities to help renters find and choose their next home with greater confidence and ease.
You Will Get To
Partner with Product, Engineering, Design, and Machine Learning (ML) to define and refine metrics that drive improvements across the rental shopping funnel, from search and discovery through contact and conversion.
Design, run, and analyze A/B tests and other experiments using statistical and causal inference methods to understand renter behavior and measure product impact.
Work with large-scale behavioral and clickstream datasets to generate insights that inform product strategy and roadmap decisions.
Translate complex analyses into clear, compelling recommendations that influence product direction and prioritization for cross-functional partners.
Own analytical projects end-to-end, from problem framing and scoping through methodology selection, execution, and communicating results and tradeoffs.
Leverage modern AI, internal platforms, and data tooling to automate and streamline workflows, improving efficiency and scaling your impact across the team.
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 $125,900.00 - $201,100.00 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 $119,600.00 - $191,000.00 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 have a Bachelor’s or Master’s degree in a quantitative field (e.g., Statistics, Economics, Engineering, Data Science) or equivalent practical experience.
You bring 3 years of experience in data science, ideally in a product-focused environment where you’ve influenced roadmap or feature decisions.
You have a strong foundation in statistics, experimentation, and causal inference, including hands-on experience designing and analyzing A/B tests.
You have experience working with event or clickstream data (e.g., Google Analytics or similar tools) and large-scale datasets to understand user behavior.
You are proficient in Python and SQL and comfortable building robust analyses in production-scale data environments.
You can independently scope and structure ambiguous problems, delivering clear, impactful insights that help stakeholders make better decisions.
You communicate complex technical concepts in an accessible way and build strong, collaborative relationships with cross-functional partners.
Here at Zillow, we val ... (truncated, view full listing at source)
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