Staff Data Scientist

Realtor.com Careers•Austin, TX

5d•Hybrid

About The Position

This is a senior technical leadership role on the Consumer Data Science & Analytics team, responsible for measurement and analytics strategy across 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.

Requirements

8+ years of experience in data science, applied statistics, or machine learning and a Bachelor’s degree (or equivalent experience) in computer science, statistics, mathematics, operations research, or a related quantitative field. (6+ years with a Master's, 3+ years with a Ph.D.)
A track record of owning ambiguous, high-visibility analytical initiatives end-to-end and driving them to measurable business impact, without close oversight.
Deep experimentation fundamentals—A/B testing, power analysis, and causal inference methods (e.g., difference-in-differences, synthetic control, propensity score matching) for settings where standard testing isn't feasible.
Strong technical fluency across SQL, Python, and modern analytics/BI tooling (e.g., Tableau, Amplitude, Snowflake).
Experience mentoring or informally leading other data scientists or analysts, and setting technical direction without formal management authority.
Comfort working in an AI-native way—using LLM APIs, agentic AI workflows, and AI-assisted development tools to accelerate analysis and build self-service data products.
Exceptional communication skills, with experience presenting complex technical findings to senior and executive stakeholders and influencing decisions at that level.

Nice To Haves

Experience spanning multiple consumer-analytics domains (e.g., content/media analytics, search/NLP, personalization, or two-sided marketplace dynamics).
Prior experience as a technical lead on a squad or pod, setting direction for a small group of data scientists or analysts.
Familiarity with LLM observability/evaluation tooling (e.g., Langfuse, Braintrust) or building data products backed by LLMs.
Experience building ETL/data pipelines (e.g., dbt, Airflow) to support scalable, self-service analytics.
Master's or Ph.D. in a quantitative field.

Responsibilities

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-service tooling—to accelerate the team's output and set the standard for how the org uses AI.
Represent the data science function in cross-functional planning, translating complex analytical and statistical findings into clear recommendations for both technical and executive audiences.