About the team
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.
About the role
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

📌 Data Scientist (Ciudad de México)
🏢 Zillow
📍 Ciudad de México