Senior / Lead / Associate Urban Data Scientist

Senior / Lead / Associate Urban Data Scientist

Dar / Urban Intelligence Lab (UIL)

Location:

Work Type:

Company Overview:

Dar, the founding member of the Sidara group, is an international multidisciplinary consulting organization specializing in engineering, architecture, planning, environment, project management, facilities management, and economics. Sidara operates in over 60 countries with 20,500 professionals, connecting people, places, and communities through innovative solutions to the world's most complex challenges.

About the Urban Intelligence Lab (UIL):

UIL is Dar’s data and AI-driven research and innovation unit focused on transforming how cities are understood, planned, and designed. Operating at the intersection of urban analytics, data science, and spatial intelligence, the lab applies cutting-edge methods - from mobility data to machine learning - to reveal hidden patterns of urban life and inform evidence-based planning and design decisions across Africa, the Middle East, and beyond.

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.

Key Responsibilities

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

Qualifications and Experience

• Bachelor’s or Master’s degree in Urban Economics, Data Science, Spatial Analysis, Geoinformatics, or a related discipline.
• 5–10 years of professional experience applying data science methods to urban, spatial, or economic questions.
• Strong programming skills in Python and its core analytical libraries (pandas, geopandas, NumPy, scikit-learn).
• Proficiency in large-scale or distributed data processing (e.g., PySpark, Dask, DuckDB, or Polars).
• Experience in designing and managing spatial databases (PostgreSQL/PostGIS).
• Solid understanding of spatial data structures, coordinate systems, and vector/raster processing.
• Demonstrated ability to translate complex data into actionable insights for urban and infrastructure policy.
• Excellent written and verbal communication skills, with experience presenting to non-technical stakeholders.

Desirable Skills

• 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).
• Experience with satellite imagery and remote sensing pipelines.
• Familiarity with containerized deployments (Docker) and API development/integration (FastAPI, Flask).
• Experience working on projects in Africa or the Global South, or with multilateral institutions (e.g., World Bank, AfDB, UN agencies).
• Interest in urban systems modelling, accessibility indices, and spatial interaction modelling.

Kind Note:

While we carefully review all applications, only candidates meeting the specified requirements will be contacted for further consideration. We appreciate your understanding and thank all applicants for their interest.