Top Real Estate Data Science Jobs

The Applied Scientist role involves developing quantitative models for decision-making under uncertainty, focusing on pricing, demand modeling, and risk management. Responsibilities include building and optimizing models, collaborating with engineering and product teams, and translating complex problems into actionable solutions.

The Sr. Data Scientist leads data-driven decision-making to enhance product development, collaborates with cross-functional teams, and champions AI-enabled reporting and data quality improvements.

The Data Scientist II estimates, develops, and implements consumer credit models related to mortgage metrics and utilizes statistical methods and machine learning. This role involves cloud computing, data analysis, and communication of findings to nontechnical teams.

The role involves building and validating Marketing Mix Models and geo-experiments, managing measurement infrastructure, and communicating insights to stakeholders.

The Senior Director of Data Science & AI leads AI strategy execution, managing a multidisciplinary team to develop and govern AI solutions, with a focus on innovation, operations, and ethical standards.

The VP of Data will lead data strategy and architecture, manage data engineering teams, and drive data-driven decision making across the organization.

Lead and grow a centralized data science team building pricing, forecasting, and predictive models. Own modeling roadmap, ensure production-grade MLOps, review technical work, partner cross-functionally, communicate with executives, recruit and develop talent, and drive measurable business impact.

As a Staff Data Engineer, you will design, prototype, and deploy data-driven solutions in a fast-paced environment, focusing on solution architecture, AI agent development, and data engineering while collaborating with product and engineering teams.

As a Staff Data Scientist, you'll develop and deploy machine learning models to enhance renter experiences and optimize property partnerships, collaborating across teams to translate data insights into actionable strategies.