Top Real Estate Data Science Jobs

Develop and deploy AI-powered analytics tools, build proactive intelligence systems, and drive leverage across the business, impacting decisions with data.

The Senior Data Scientist will drive revenue optimization through experimentation, data modeling, and pricing intelligence, partnering with product and finance teams to deliver insights and recommendations for pricing strategies.

The role involves designing data models, developing ETL pipelines, creating BI solutions, and collaborating with clients to support business analytics initiatives.

Design and deliver ML and algorithmic solutions on large property and consumer datasets. Perform feature engineering, model development (supervised/unsupervised/deep learning), data QA, stakeholder reporting, and lead technical data science projects across teams.

Lead a team of advanced product analysts, conduct data analysis and statistical modeling, design experiments, and develop reporting tools to drive product decisions and business performance.

Design, test, and deploy time‑series and econometric forecasting models; own end‑to‑end model lifecycle (feature engineering, deployment, monitoring); analyze large datasets to surface drivers; translate results into clear insights for leadership; partner cross‑functionally to scale forecasting capabilities and improve shared tools and pipelines.

The Data Scientist will design and implement models for predictive analytics, collaborate with stakeholders, and communicate insights, enhancing decision-making processes within the company.

As a Senior Data Science Engineer, you will develop machine learning algorithms and statistical models, perform data analysis, and build data-driven products to enhance real estate solutions.

The role involves analyzing complex datasets, developing predictive models, and translating business questions into analytical insights to drive strategic decisions at Ventas.

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.