At a Glance
- Tasks: Design spatial economic models and analyse urban performance using advanced data science techniques.
- Company: Join Oxford Economics, a leader in city and regional economic analysis.
- Benefits: Enjoy private healthcare, flexible working, and enhanced parental leave.
- Other info: Collaborative environment with opportunities for career growth and global client exposure.
- Why this job: Make an impact on urban development with innovative location intelligence research.
- Qualifications: Degree in economics, geography, or data science; strong Python skills required.
The predicted salary is between 50000 - 65000 £ per year.
Oxford Economics is a global leader in city and regional economic analysis, combining advanced data science with deep economic expertise. Our Cities Team forecasts thousands of locations worldwide, integrating economic models with geospatial analytics to drive decision-making across real estate, financial services, government, and beyond. We are seeking a Spatial Data Scientist to contribute to our growing portfolio of location intelligence research and geospatial modelling work. 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.
Key Responsibilities
- 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.
Skills, Knowledge & Expertise
We are open to candidates at an early-to-mid career stage, including those with postgraduate research experience, who can demonstrate strong quantitative and programming skills alongside an interest in cities, regions, and economic analysis. We are looking for someone technically capable, curious, and keen to develop in a research-led environment.
- 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.
Desirable extras:
- 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, or experience building interactive web tools.
Job Benefits
Here are some of the benefits we offer in the UK to ensure you feel valued, supported, and thrive at work:
- Private Healthcare
- Employee Assistance Program
- Enhanced Maternity and Paternity Leave
- Workplace Nursery Scheme
- Cycle to Work Scheme
- Hybrid/Flexible Working
- Team Gatherings and Connection Boost!
Spatial Data Scientist in London employer: Oxford Economics
Oxford Economics is an exceptional employer, offering a dynamic work environment in the heart of London where innovation meets economic analysis. With a strong focus on employee growth, we provide opportunities for professional development through collaboration with experts in economics and data science, alongside a comprehensive benefits package that includes private healthcare and flexible working arrangements. Join us to contribute to impactful projects that shape the future of cities and regions while enjoying a supportive and inclusive workplace culture.
StudySmarter Expert Advice🤫
We think this is how you could land Spatial Data Scientist in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals on LinkedIn. We can’t stress enough how important it is to make those connections; you never know who might have the inside scoop on job openings.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your spatial data projects, analyses, and any cool visualisations you've made. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding the latest trends in spatial data science. We recommend practising common interview questions and even doing mock interviews with friends or mentors to build your confidence.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are genuinely interested in joining our team and contributing to our exciting projects.
We think you need these skills to ace Spatial Data Scientist in London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Spatial Data Scientist role. Highlight your experience with Python, spatial data analysis, and any relevant projects that showcase your skills in urban economics and geospatial modelling.
Showcase Your Technical Skills:We want to see your technical prowess! Include specific examples of how you've used Python and relevant libraries like geopandas or shapely in your previous work. Don't forget to mention any experience with machine learning techniques or data pipelines.
Communicate Clearly:Remember, you'll be preparing outputs for non-specialist audiences. So, practice distilling complex technical findings into clear, accessible language. This will show us you can bridge the gap between data science and real-world applications.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates about the hiring process!
How to prepare for a job interview at Oxford Economics
✨Know Your Spatial Data Science
Make sure you brush up on your knowledge of spatial data science and economic analysis. Be prepared to discuss specific projects or experiences where you've applied Python for geospatial analysis, and be ready to explain the methodologies you used.
✨Showcase Your Technical Skills
Highlight your proficiency in Python and any relevant packages like geopandas or shapely. Bring examples of your work, such as code snippets or project summaries, to demonstrate your ability to build data pipelines and conduct advanced spatial analyses.
✨Communicate Clearly
Since you'll need to distil complex technical findings into clear outputs, practice explaining your past projects to a non-specialist audience. This will show your ability to communicate effectively across teams, which is crucial for this role.
✨Express Your Curiosity
Demonstrate your genuine interest in cities, regions, and urban economics during the interview. Share insights or questions about current trends in location intelligence or urban development to show that you're engaged and eager to learn more.
