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The role sits at the intersection of economics, spatial data science, and geospatial analysis. You will work alongside economists, data scientists, and software engineers to develop innovative datasets, forecasting methodologies, and location intelligence products that help clients understand how cities and regions develop. The role combines applied quantitative analysis, spatial modelling, data engineering, and product development — with exposure to a global client base across both subscription services and bespoke consultancy projects.
Job Responsibility
Design and build spatial economic models to analyse urban economic performance, spatial disparities, and geographic drivers of economic activity
Contribute to the development of city, regional, and sub-national forecasting methodologies
Build and maintain Python-based data pipelines to process, integrate, and analyse large geospatial datasets
Work with a wide range of data sources, such as Census data, satellite-derived indicators, global buildings and transport datasets, administrative boundaries, and Oxford Economics forecasts
Apply machine learning techniques for classification and regression tasks on spatial and economic data
Conduct advanced spatial analyses, like geographically weighted regression, spatial autocorrelation analysis, and spatial autoregressive modelling
Support the development of scalable location intelligence products, digital tools, and analytical workflows
Contribute to location intelligence tools and products, including map-based visualisations and interactive dashboards
Prepare clear written outputs to communicate technical findings to non-specialist audiences
Collaborate across the Cities Team and with other Oxford Economics teams on cross-functional projects
Requirements
Degree in economics, geography, data science, statistics, or a closely related discipline (postgraduate preferred)
Strong proficiency in Python for spatial data analysis. Experience working with packages like geopandas, shapely, polars, folium, duckdb, OSMnx and developing modern version-controlled workflows using Git or similar systems
Experience working with vector/raster data, spatial indexing, and coordinate reference systems Experience building reproducible analytical workflows and data pipelines
Knowledge of socioeconomic concepts relevant to sub-national analysis is highly desirable
Experience working with APIs, cloud-hosted datasets, or large tabular/geospatial data at scale
Competent with data visualisation tools
Strong written communication: ability to distil technical results into clear, accessible outputs
Ability to manage own workload across multiple concurrent projects
Genuine interest in cities, regions, urban economics, location intelligence, or related fields
Nice to have
Knowledge of economics, economic geography, urban economics, regional economics, or economic forecasting
Experience with geospatial APIs (Mapbox, Google Maps Platform, OpenStreetMap, R5)
Exposure to EViews or similar econometric software