Junior Data Scientist

Job Description

Overview

Job Title

Junior Data Scientist Job Description Summary

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. Job Description

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 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.

Preferred Qualifications

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

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.

2 t o 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.
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.

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