Senior / Lead / Associate Urban Data Scientist

Description

• Collaborate with cross-functional teams to translate findings into policy-relevant insights, dashboards, and visualizations.
• Mentor junior analysts to provide technical and strategic guidance on their work.
• Conduct big data analyses leveraging distributed processing tools (PySpark, Dask, DuckDB, GeoParquet).
• Apply urban economics frameworks (land use, accessibility, productivity, agglomeration) to real-world datasets.
• Build reproducible workflows for data cleaning, feature engineering, and spatial modeling.
• Contribute to applied research outputs, white papers, and innovation projects within UIL.
• Design and implement scalable data pipelines for large geospatial and mobility datasets.

Requirements

Dar / Urban Intelligence Lab (UIL)

We seek a Senior / Lead / Associate Urban Data Scientist to advance UIL’s growing portfolio of city analytics. The ideal candidate will combine expertise in human mobility and urban economics with strong analytical and technical skills, leading data-driven studies that explore how people, infrastructure, and economic activity interact within cities.

• Excellent written and verbal communication skills, with experience presenting to non-technical stakeholders.
• 10-15 years of professional experience applying data science methods to urban, spatial, or economic questions.
• Integrate and analyze multi-source data — including mobility, POI, census, satellite, and transport network datasets — to extract insights on urban form and function. Should have experience handling multi-dimensional and multimodal data.
• Familiarity with containerized deployments (Docker) and API development/integration (FastAPI, Flask).
• Demonstrated ability to translate complex data into actionable insights for urban and infrastructure policy.
• Solid understanding of spatial data structures, coordinate systems, and vector/raster processing.
• Experience with satellite imagery and remote sensing pipelines.
• Proficiency in large-scale or distributed data processing (e.g., PySpark, Dask, DuckDB, or Polars).
• Background in econometrics, machine learning, or causal inference applied to spatial or economic data.
• Familiarity with cloud platforms (AWS, GCP, or Azure) and workflow orchestration tools (e.g., Airflow, Prefect, or similar).
• Strong programming skills in Python and its core analytical libraries (pandas, geopandas, NumPy, scikit-learn).
• Experience working on projects in Africa or the Global South, or with multilateral institutions (e.g., World Bank, AfDB, UN agencies).
• Experience in designing and managing spatial databases (PostgreSQL/PostGIS).
• Bachelor’s or Master’s degree in Urban Economics, Data Science, Spatial Analysis, Geoinformatics, or a related discipline.

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance