Real Estate Data Scientist - Remote

Duties and Responsibilities

• Develop and deploy predictive models for site selection, sales forecasting, cannibalization, and market potential.
• Build and maintain machine learning models using regression, classification, clustering, optimization, and spatial modeling techniques.
• Apply spatial statistical methods (e.g., spatial regression, geographically weighted regression, spatial autocorrelation) to capture geographic variation in demand drivers.
• Develop trade area and customer draw models (e.g., Huff/gravity models) to estimate market share and competitive impact.
• Incorporate spatial features such as proximity, co-tenancy, demographics, traffic patterns, and nearby store performance into predictive models.
• Design methodologies for forecasting store performance under various scenarios, including spatial and competitive effects.
• Continuously monitor and improve model performance and accuracy.

Data Engineering & Automation

• Design scalable data pipelines integrating real estate, customer, demographic, sales, and geospatial datasets (parcel, census, traffic, mobility, POI data).
• Perform geospatial data processing including geocoding, spatial joins, coordinate transformations, and spatial indexing (e.g., H3 or similar frameworks).
• Write efficient SQL and Python workflows to automate recurring analyses, spatial feature engineering, and model refreshes.
• Ensure data quality, consistency, and reproducibility across analytical outputs, including alignment of spatial boundaries and geographic hierarchies.

Real Estate Strategy & Decision Support

• Partner with Real Estate teams to support site selection, market entry, relocations, and closures.
• Conduct drive-time and network-based trade area analyses to assess accessibility and market reach.
• Conduct market coverage and white space analysis to identify expansion opportunities and underserved areas.
• Build location-allocation and network optimization models to determine optimal site placement.
• Quantify cannibalization and competitive effects using spatial overlap and proximity-based modeling.
• Provide quantitative insights for Real Estate Committee (REC) evaluations and executive decisions.
• Develop scoring frameworks and decision tools to prioritize opportunities.

Visualization & Communication

• Create clear, compelling visualizations and dashboards (Tableau, Power BI, or similar) to communicate insights.
• Develop interactive geospatial visualizations including trade area maps, performance heatmaps, and market opportunity analyses.
• Present analytical findings and recommendations to senior leadership and non-technical stakeholders.

Experimentation & Innovation

• Design and execute experiments (A/B tests, quasi-experimental designs) to evaluate real estate strategies.
• Implement geo-based testing frameworks (e.g., test vs. control markets) to measure impact of site decisions.
• Apply causal inference methods (e.g., difference-in-differences, synthetic control) accounting for geographic spillovers.
• Explore new data sources (e.g., mobility, foot traffic) and modeling techniques to enhance predictive capabilities.
• Contribute to building a best-in-class real estate analytics capability.

Cross-Functional Collaboration

• Work closely with GIS, Data Engineering, Finance, Marketing, and IT teams to align data and models.
• Partner with GIS teams to ensure alignment between spatial analysis, mapping, and production data pipelines.
• Translate business problems into analytical solutions and actionable insights.

Qualifications

Required:

• PhD or MS in Statistics, Applied Mathematics, Computer Science, Geography, Urban Planning, or a related field.
• Experience with spatial statistics, geostatistics, and geospatial data engineering.
• Strong background in machine learning, predictive modeling, and optimization techniques.
• Proficiency in Python, R, SQL, and Tableau or similar visualization tools.
• Experience with spatial databases and geospatial data processing.
• Excellent communication and presentation skills.

Preferred:

• Experience with big data technologies (Hadoop, Spark, etc.).
• Experience with spatial databases and geospatial data processing.
• Experience with spatial analysis software (ArcGIS, QGIS, etc.).
• Experience with spatial econometrics and spatial statistics.
• Experience with causal inference methods and experimental design.

Benefits

The anticipated salary range for this position is $98,500-$147,800 depending on location, knowledge, skills, education and experience. This position is also eligible for an annual discretionary bonus. In addition, we offer comprehensive and competitive benefits to Associates (and their families) such as medical, dental, vision, life insurance, short-term and long-term disability. Eligible Associates are able to enroll in our company’s 401k plan. Associates will accrue paid time off up to 236 hours per year (inclusive of PTO, floating holidays, and paid holidays).