[Hiring] Data Scientist @Zillow

Role Description

As a Data Scientist within the Zillow Home Loans (ZHL) Data Science & Analytics team, you'll work across the entire home buying customer journey — from a home shopper's first touchpoint on Zillow.com through to a funded loan. The team works closely with ZHL's mortgage and product leadership to uncover opportunities, measure impact, and build a best-in-class integrated financing experience for Zillow customers. You'll contribute to that mission across the entire funnel, from web-scale A/B tests to deep-funnel mortgage operations analyses.

At Zillow, Data Scientists develop insights and models that shape products and services, balancing rigor and speed to drive methodologically sound outputs that directly impact key decision points. You own scoped projects end-to-end: you translate business questions into analysis plans, execute independently, and communicate results clearly to stakeholders. This is an opportunity to grow your craft while working on problems that directly affect how millions of people find, buy, and finance their next home.

You Will Get To

• Design, implement, and read out causal analyses across the Zillow.com-to-ZHL funnel — from web-scale experiments to deep-funnel small-population tests — using a variety of inference methods to achieve appropriate statistical rigor.
• Apply statistical modeling and advanced analytics to predict customer behavior, explain the impact of product and operational changes, and surface opportunities in the digital mortgage experience.
• Partner with product and engineering teams to define measurement frameworks, pull relevant data, and translate findings into clear recommendations for technical and non-technical audiences.
• Prototype analytical pipelines for high-impact analyses and see them through to hardened, production-grade data sources used across the company — partnering with shared infrastructure teams and contributing to team standards along the way.
• Collaborate with fellow Data Scientists and the Data Analyst on the team to improve tooling, documentation, and analytical quality.

This role has been categorized as a teleworker position. Teleworkers 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. Employees may live anywhere in Mexico, with availability to travel to Mexico City, as we recommend attendance at occasional office events.

In addition to a competitive base salary and benefits, this position is also eligible for equity awards based on factors such as experience, performance and location.

Qualifications

• Strong proficiency in SQL and Python.
• Demonstrated experience with statistical modeling and inference; ability to choose simple, proven methods when appropriate.
• Experience delivering analyses or models that are used repeatedly by the business over months.
• Ability to communicate clearly and work cross-functionally with technical and non-technical partners.

Requirements

• BS +5 or more years, MS +3 or more years, or PhD +1 or more years of industry experience in data science, statistics, applied science, or a related quantitative field.
• Experience with A/B testing and experimentation design, including power analysis and understanding of multiple testing considerations.
• Experience with causal inference methods in business settings (e.g., diff-in-diff, synthetic controls, propensity score matching, uplift modeling, or similar quasi-experimental designs).
• Exposure to customer-facing web or app analytics (e.g., clickstream, funnel analysis, engagement metrics).
• Familiarity with statistical or ML modeling packages and how to apply them to business problems (e.g., regression, classification, clustering, forecasting).
• Experience with data engineering pipelines and scheduling tools (Airflow, dbt, Spark, or similar).
• Exposure to mortgage, fintech, or ecommerce domains is a plus but not required.

Benefits

• Competitive base salary.
• Benefits package.
• Equity awards based on factors such as experience, performance, and location.