Senior Data Scientist, RDC Next

Realtor.com Careers•Austin, TX

1d•Hybrid

About The Position

This role serves as a senior analytics partner on RDC Next, with a focus on Realtor.com Plus — Realtor.com's partnership platform connecting MLS organizations, their agent members, and consumers in a single collaborative home search experience. The position owns experimentation, business analytics, and reporting across both sides of the platform, delivering actionable insights that directly influence the product roadmap, market launch strategy, and broader business strategy. By partnering with cross-functional teams including Product, Engineering, Finance, Industry Marketing, and MLS partner data teams, the role ensures alignment on key business objectives, facilitates rapid innovation, and supports scalable solutions through advanced analytics, agent and consumer behavior insights, and performance measurement. The role is critical in surfacing high-impact opportunities as the network scales to new markets, improving data quality, and enabling self-service reporting to empower teams across the organization.

Requirements

Bachelor’s Degree in Computer Science, Statistics, Mathematics, Economics or a related quantitative field
5+ years of experience as a Data Scientist or Analytics professional, with a strong track record of delivering impactful, data-driven solutions — experience supporting a consumer-facing digital product is preferred
Strong proficiency in SQL and in Python or R, along with related data analysis libraries (e.g. Pandas, NumPy, scikit-learn)
Hands-on experience with cloud data warehouses (e.g. Snowflake) and with BI or product analytics tooling (e.g. Tableau, Looker, Amplitude)
Expertise in data visualization and storytelling to effectively communicate complex findings
Strong understanding of statistical analysis, hypothesis testing, and experimental design
Demonstrated hands-on use of AI tools in day-to-day analytical work, plus the curiosity and self-direction to keep learning as new AI capabilities emerge — we expect this role to help define what AI-enabled analytics looks like on our team
Product sense and a strategic mindset that enables one to learn how the business operates, understand consumer and stakeholder pain points, and deliver recommendations that drive measurable value
Strong communication skills to effectively communicate complex information and strategies to non-technical audiences

Nice To Haves

Experience building with LLM APIs, prompt engineering, retrieval-augmented generation (RAG), or agentic workflows in a data or analytics context
Experience working with large-scale behavioral or clickstream data and modern data stack tooling (e.g. dbt, Airflow)
Experience partnering with product teams in real estate, marketplaces, or another two-sided consumer business

Responsibilities

Collaborate with product, design, engineering, and marketing stakeholders across Realtor.com to identify the consumer and business questions where data science can add the most value, and shape product strategy with the answers.
Own analytics end to end for consumer product initiatives — define success metrics and measurement plans, partner with engineering on instrumentation, run the deep-dive analysis, and land clear recommendations with product leadership.
Apply advanced statistical and machine learning techniques to consumer behavior and clickstream data to surface the drivers of engagement, retention, and conversion, and translate those findings into strategic recommendations.
Design and analyze experiments (A/B and quasi-experimental), and raise the bar on how the organization measures impact — from sound test design through correct interpretation of results.
Build and maintain the datasets, metric definitions, pipelines, and dashboards that make consumer analytics trustworthy and self-serve for partner teams.
Mentor other analysts and data scientists, and help set the analytical standards, documentation habits, and code quality expectations for the team.
Partner closely with Data Engineering and Machine Learning Engineering teams to productionize models and improve the data foundation the whole organization relies on.
Use AI tooling — LLM assistants, AI coding and analysis agents, and internal AI platforms — to accelerate every stage of the analytics lifecycle, from data discovery and pipeline development to exploratory analysis and communicating results.
Design and build AI-enabled analytics: LLM-powered synthesis of unstructured inputs such as consumer feedback and reviews, semantic search across data assets, and conversational or agentic interfaces that let stakeholders answer their own questions.
Apply sound judgment and practical guardrails to AI-assisted work — validating AI-generated code and output, recognizing where models are unreliable, handling sensitive data appropriately, and being transparent about where AI was used.
Stay current as the tooling evolves: evaluate new AI capabilities, pressure-test them on real problems, share what works with the team, and help shape how the broader analytics organization adopts AI.