Data Scientist (Geospatial)

Responsibilities

• Machine Learning Solution Design
• Design end-to-end machine learning architectures that support geospatial analytics and demand forecasting use cases, including the organisation's Spatial Modelling Engine.
• Define robust, maintainable, and extensible data pipelines, feature engineering strategies, and model-serving frameworks.
• Ensure that architectural decisions account for the long-term nature of infrastructure planning, where model outputs must remain interpretable and auditable over multi-year periods.

• Machine Learning Development and Implementation
• Develop, test, and deploy machine learning models and geospatial analytics solutions within a production environment.
• Build and maintain data pipelines that integrate a variety of data sources, including housing development information, demographic records, migration patterns, land-use plans, and accessibility metrics.
• Collaborate with engineering and platform teams to ensure that models are reliably operationalised and continuously monitored over time.

• Machine Learning Optimisation and Geospatial Analytics
• Develop and enhance predictive and spatial models that forecast future education demand across Singapore's planning landscape.
• Apply suitable techniques based on the problem context, including spatial regression, time-series forecasting, agent-based modelling, and deep learning.
• Continuously assess model performance, validate outputs against ground-truth data, and refine modelling approaches to improve forecast accuracy and reliability.

Qualifications

• At least 3–5 years of hands-on experience in data science or a related field, with a demonstrated track record of delivering machine learning solutions in production.
• Prior experience working with geospatial data and tools is strongly preferred.
• Experience in areas involving demographic modelling, urban planning, or public-sector analytics would also be highly relevant.
• Familiarity with Singapore's planning context, including URA Master Plan data, HDB housing pipelines, or similar datasets, would be an advantage.

Required Skills

• Proficiency in Python and relevant data science libraries, such as scikit-learn, PyTorch, or TensorFlow.
• Knowledge of geospatial tools and frameworks, such as GeoPandas, QGIS, PostGIS, or ArcGIS, would be an added advantage.
• Strong SQL skills and experience working with cloud data platforms, such as AWS, GCP, or Azure.
• Familiarity with the full machine learning lifecycle, including data wrangling, feature engineering, model evaluation, deployment, and monitoring.
• Comfortable applying advanced machine learning techniques, such as ensemble learning, regularisation, agent-based modelling, and forecasting.
• Strong communication skills, with the ability to clearly present findings and recommendations to non-technical stakeholders.
• Ability to collaborate effectively within a cross-functional team environment.