Key Responsibilities

Real Estate & Urban Economic Analysis (45%)

• Conduct rigorous quantitative analysis on commercial real estate markets, synthesizing property, macroeconomic, and urban data to surface market trends, structural shifts, and investment-relevant insights.

• Apply econometric and statistical methods (time series modeling, regression, spatial econometrics, or similar) to real estate and labor market questions in support of QIG research products.

• Integrate geospatial data and methods into analytical workflows: working with Census geographies, parcel data, land use classifications, walkability or transit metrics, demographic overlays, and similar inputs to enrich market analysis.

• Contribute to the development of novel datasets and indicators that advance QIG's analytical edge, including working closely with the Head of Data Science & Geospatial Analytics to specify and build integrated data products combining proprietary CRE data with public and third-party sources.

• Support the QIG team on ad hoc analytical requests from Americas Research, the Global Think Tank, and senior stakeholders, producing clean, well-documented, and reproducible outputs.

Data Engineering & Pipeline Maintenance (35%)

• Build andmaintainautomated data pipelines for ingesting, transforming, and storing CRE and macroeconomic datasets used in analytical modelsand reoccurring analysis.

• Ensure data integrity and consistency across QIG inputs and outputs through validation, quality control procedures, and structured data interfaces.

• Perform exploratory data analysis and profiling on raw and processed datasets tovalidatepipeline outputs and identifyanomalies or inconsistencies.

• Partner with PRI (Property Research & Intelligence), TDS (Technology & Data Solutions), and the GIS team to ensure governance of time series and geospatial data, particularly as geography-based competitive sets evolve.

• Serve as a knowledgeable liaison to TDS: translating analytical requirements into engineering specifications, tracking the status of data requests in the TDS backlog, and validating outputs against analytical expectations.

Documentation, Integration & Infrastructure (20%)

• Develop andmaintaininternal documentation covering data sources, model architecture, data flows, and diagnostic procedures, with attention to field-level lineage and traceability.

• Serve as the team's subject matter expert on integration and processing of internal, third-party vendor, and public datasets (e.g., Census TIGER, IPUMS, LODES, NLCD, Overture Maps), and advise on cleaning, normalization, andappropriate analyticalapplications.

• Monitor the evolution of third-party data products; assess their fit against QIG analytical requirements and produce intake specifications when new sources are approved for integration.

• Support the adoption of emerging analytical technologies (including ML/AI methods and advanced data infrastructure patterns) through hands-on prototyping and coordination with TDS whereappropriate.

Qualifications 

• Bachelor’s degree in Economics, Data Science, Real Estate, Applied Economics, Geography, Urban Planning or any closely related field with quantitative emphasis. A master’s degree is preferred and a doctoral degree is a plus. 

• 2 to 6 years of experience in a research, analytical, or data science role, preferably in a real estate, urban policy, planning, or economic research context. 

• Strong command of quantitative methods: regression, time series analysis, spatial econometrics, or comparable approaches applied to real estate or urban economic questions. 

• Working knowledge of geospatial data and methods: experience with GIS tools (ArcGIS, QGIS, or programmatic approaches via R or Python), familiarity with spatial data formats and concepts, and comfort integrating geographic context into analysis. 

• Proficiency in Python and/or R for data analysis, modeling, and pipeline construction; working knowledge of SQL. Familiarity with cloud platforms (Azure, AWS) and version control is a plus. 

• Experience working with public datasets commonly used in urban and real estate research: Census products (ACS, TIGER, LODES), BLS, IPUMS, or similar. 

• Ability to produce clean, well-documented, reproducible analytical work and communicate findings clearly to both technical and non-technical audiences. 

• Comfortable operating in a cross-functional environment, working both independently and alongside engineering and research teams on iterative deliverables. 

• Genuine intellectual interest in urban economics, commercial real estate markets, and the spatial dimensions of economic activity. 

• Comfortability in communicating analysis, methods and related topics with related teams and immediate management.