Junior Data Scientist

Cushman & Wakefield•Washington, DC

2d•$114,750 - $135,000•Onsite

About The Position

This role sits at the intersection of real estate economics, urban analysis, and data science. The Junior Data Scientist will support the development and evolution of Cushman and Wakefield Quantitative Insight Group’s (QIG) analytical capabilities by producing rigorous, insight-driven work on commercial real estate markets across the Americas. This position will report to the Head of Data Science and Geospatial Analytics. The ideal candidate is a practitioner who thinks first like an economist, real estate analyst, or quantitative urban planner, and who brings the technical skills to build and operate the data infrastructure their own work requires. This is not primarily an engineering role, though the ideal candidate will possess data engineering knowledge, skills, and abilities. The Analyst will spend most of their time doing substantive analytical and research work: synthesizing complex datasets, identifying market patterns and anomalies, and producing outputs that inform Cushman & Wakefield’s House View, including elements that are unique to QIG, and related analytical products for key clients. At the same time, the candidate should be comfortable constructing and maintaining data pipelines, working fluently in Python and/or R and SQL, and collaborating closely with Technology & Data Solutions (TDS) as a knowledgeable and credible partner.

Requirements

Bachelor’s degree in Economics, Data Science, Real Estate, Applied Economics, Geography, Urban Planning or any closely related field with quantitative emphasis.
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.
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.

Nice To Haves

A master’s degree is preferred and a doctoral degree is a plus.
Familiarity with cloud platforms (Azure, AWS) and version control is a plus.

Responsibilities

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.
Build and maintain automated data pipelines for ingesting, transforming, and storing CRE and macroeconomic datasets used in analytical models and 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 to validate pipeline outputs and identify anomalies 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.
Develop and maintain internal 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, and appropriate analytical applications.
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 where appropriate.