M06 - Data Scientist

Overview Responsibilities Requirements Analysis & Solution Design Collaborate with planners, analysts, and stakeholders to understand business requirements and translate them into scalable data science solutions. Conduct exploratory data analysis to uncover insights and inform solution design. Design analytical approaches that balance technical robustness with operational practicality. Machine Learning Solution Development Design end-to-end machine learning architectures for geospatial analytics and demand forecasting. Define feature engineering strategies, model architectures, and model serving frameworks. Ensure solutions are scalable, maintainable, interpretable, and auditable for long-term planning. Model Development & Deployment Develop, test, deploy, and maintain machine learning models in production environments. Build and manage data pipelines integrating multiple data sources, including demographic, housing, migration, land-use, and accessibility datasets. Collaborate with data engineers and platform teams to operationalise, monitor, and maintain ML solutions. Geospatial Analytics & Model Optimisation Develop predictive models for geospatial analysis and education demand forecasting. Apply statistical modelling, spatial regression, time-series forecasting, agent-based modelling, deep learning, and other advanced machine learning techniques where appropriate. Continuously evaluate model performance, validate predictions, and optimise forecasting accuracy. Requirements Experience Minimum 3–5 years of hands-on experience in Data Science, Machine Learning, or a related field. Proven experience delivering production-grade machine learning solutions. Experience working with geospatial data is highly preferred. Experience in demographic modelling, urban planning, public sector analytics, or spatial modelling is an advantage. Familiarity with Singapore planning datasets (e.g., URA Master Plan, HDB housing data) is beneficial. Technical Skills Strong proficiency in Python and machine learning libraries such as Scikit-learn, PyTorch, or TensorFlow. Strong SQL skills for data querying and transformation. Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP). Good understanding of the complete machine learning lifecycle, including: Data preparation and feature engineering Model training and evaluation Model deployment and monitoring Experience with advanced machine learning techniques such as: Time-series forecasting Ensemble learning Regularisation methods Agent-based modelling Deep learning Experience with geospatial technologies such as GeoPandas, PostGIS, QGIS, or ArcGIS is an advantage. Soft Skills Strong analytical and problem-solving abilities. Excellent communication skills with the ability to present technical findings to non-technical stakeholders. Strong stakeholder management and cross-functional collaboration skills. Ability to translate business problems into practical, scalable machine learning solutions. Self-motivated, proactive, and passionate about leveraging AI and data science to solve real-world public sector challenges. Comfortable working in Agile, multidisciplinary teams with evolving business needs.