Land Data Scientist

Job Description

1. Build and maintain a data-driven framework for evaluating land potential and constraints.
2. Analyze site-level and market-level metrics, including:
3. Density/yield potential (units per acre, FAR)
4. Access to employment, transit, and amenities
5. Market rent trends and absorption
6. Construction cost indexes and land price comparables
7. Environmental and geographic risk factors (flood, slope, soil, etc.)
8. Use GIS and data visualization tools to map and score potential sites.
9. Develop a consistent “site scorecard” or grading model to rank opportunities by investment potential.

1. Integrate demographic, economic, and housing data (e.g., census, CoStar, ESRI, MLS, local planning data) to identify growth corridors and emerging markets.
2. Analyze proximity metrics such as commute times, walkability, school quality, and employment density.
3. Maintain databases of historical and current land transactions for benchmarking.

1. Provide data-backed site summaries for all land under consideration.
2. Collaborate with acquisitions and finance teams to refine land pricing assumptions, density yields, and potential return profiles.
3. Identify off-market areas or submarkets that align with company strategy based on data trends.

1. Design repeatable workflows for data collection, site screening, and scoring.
2. Implement or manage mapping and analytics tools (e.g., GIS, Reonomy, LandVision, or custom dashboards).
3. Continuously improve analytical methodologies to increase accuracy and speed of decision-making.

1. 4+ years of experience in data analytics, real estate market research, or land/site evaluation.
2. Strong quantitative and spatial analysis skills.
3. Proficiency with GIS tools (ArcGIS, QGIS, or similar) and data analysis platforms (Excel, SQL, Python, R, or BI tools).
4. Experience with real estate or urban data (zoning, demographics, rents, permits, land use).
5. Ability to communicate complex findings clearly through visuals, dashboards, and concise summaries.

1. Degree in Urban Economics, Geography, Data Science, Real Estate, or related field.
2. Familiarity with CoStar, Reonomy, LandVision, or similar land intelligence platforms.
3. Understanding of multifamily development fundamentals (density, parking, yield-on-cost).

1. Speed and accuracy of site evaluations.
2. Predictive value of site scoring model (how well-chosen sites perform post-acquisition).
3. Quality and clarity of analytics reports delivered to acquisitions team.
4. Contribution to identifying high-potential land before competitors.

1. Competitive salary and performance-based bonus
2. Health, dental, and vision insurance
3. Paid time off and professional development opportunities