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About the Role
This role involves collaborating with planners and stakeholders to translate complex infrastructure and space planning needs into technical specifications. You will design and implement end-to-end machine learning architectures, specifically focusing on geospatial and demand forecasting to support long-term planning initiatives.
Responsibilities
- Translate complex business requirements into analytical and technical specifications.
- Conduct exploratory data analysis to inform solution design.
- Design end-to-end machine learning architectures for geospatial and demand forecasting.
- Define data pipelines, feature engineering strategies, and model serving frameworks.
- Develop, test, and deploy machine learning models and geospatial analytics in production.
- Build and maintain data pipelines integrating diverse datasets like housing, demographics, and land-use plans.
- Collaborate with engineers to ensure reliable model operationalisation and monitoring.
- Develop and refine predictive and spatial models for education demand forecasting.
- Evaluate model performance and iterate on approaches to improve accuracy.
Requirements
- Strong hands-on experience in data science or a related field.
- Demonstrable track record of delivering machine learning solutions in production.
- Experience with geospatial data and tools.
- Experience in demographic modelling, urban planning, or public sector analytics.
- Familiarity with the Singapore planning context, including URA Master Plan data and HDB housing pipelines.
Skills
- Python
- scikit-learn
- PyTorch
- TensorFlow
- GeoPandas
- QGIS
- PostGIS
- ArcGIS
- SQL
- Cloud data platforms (AWS, GCP, or Azure)
- Full ML lifecycle management
- Ensemble learning
- Regularisation
- Agent-based modelling
- Time-series forecasting
- Communication skills
- Cross-functional collaboration