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.