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Staff Data Scientist

Realtor.com

Austin, Texas, United States · staff
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Austin, Texas, United States
Location
Staff
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Realtor.com
Employer
Data Scientist jobs
Category

Recognized as the No. 1 site trusted by real estate professionals, Realtor.com® has been at the forefront of online real estate for over 25 years, connecting buyers, sellers, and renters with trusted insights and expert guidance to find their perfect home. Through its robust suite of tools, Realtor.com® not only makes a significant impact on the real estate industry at large, but for consumers, navigating the biggest purchase they will make in their life, by providing a user experience that is easy to use, easy to understand, and most of all, easy to make decisions.

Join us on our mission to empower more people to find their way home by breaking barriers to entry, making the right connections, and building confidence through expert guidance. Who We Are Realtor.com® is your one stop shop for homebuyers, sellers, and renters. We make it easy for consumers to choose a place to live whether it's renting a condo or buying new construction. For over 20 years, millions of home shoppers and renters have turned to Realtor.com® to find their dream home or rental.

Top Reasons to Apply: You'll set the data science strategy for one of the highest-traffic sites in real estate, with visibility at VP+ leadership—and the backing to fix things at the root: better tracking, scalable pipelines, and higher data quality standards across the company, not just your own team. This is a real staff-level IC role, not a stepping stone into management. You'll go beyond standard A/B testing into geo-experiments, synthetic controls, and other causal inference methods for problems that don't have a textbook answer, while mentoring senior data scientists and setting the team's technical direction.

Industry-Leading AI and Modern Tech Stack: Realtor.com is recognized by vendors as a top-tier leader in AI adoption. You'll work daily with a modern analytics stack—Python, Snowflake, Amplitude, dbt, and Airflow—plus advanced AI-native tools like Streamlit, MCPs, and Windsurf, at the frontier of how the industry applies AI to data science. About the Role This is a senior technical leadership role on the Consumer Data Science & Analytics team, responsible for measurement and analytics strategy across http://Realtor.com 's consumer product experiences—spanning content and visualization, search and personalization, and other high-priority consumer initiatives.

As a Staff Data Scientist, you'll operate as a player-coach: owning the most ambiguous, highest-visibility analytical problems end-to-end while setting the technical bar and mentoring other data scientists on the team. You'll partner directly with Product, Engineering, and senior leadership to define what gets measured and why, shape multi-quarter experimentation and measurement roadmaps, and exercise the judgment to say no to low-value ad hoc requests in favor of the analyses that move the business..

What You'll Do Own a high-visibility, cross-team initiative end to end—from framing the business question through method selection, execution, and socializing recommendations with senior stakeholders—with minimal oversight. Set the analytical and experimentation standards for the team: design the test-and-learn frameworks, causal inference approaches, and metrics definitions other data scientists build on.

Partner with Product and Engineering leadership to shape the measurement and analytics roadmap across consumer initiatives (e.g., AI-backed search & recommendations, ), reasoning independently about tradeoffs like inventory vs. user preference, cold-start, and test design. Mentor and provide technical guidance to Senior and mid-level data scientists, reviewing analytical approaches and elevating the rigor of the team's work.

Identify and prioritize the highest-leverage analytical opportunities across the consumer product portfolio, protecting time and focus for the initiatives with the greatest business impact. Champion and model AI-native ways of working—agentic workflows, LLM-assisted analysis, and self

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