Job Description

Requirements Analysis: Work closely with planners, analysts, and stakeholders across MOE to understand long-term infrastructure and space planning needs, translating complex business requirements into well-defined analytical and technical specifications. Conduct exploratory data analysis to surface insights that inform solution design, and propose scalable, fit-for-purpose approaches that balance analytical rigour with operational practicality.

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

ML Development and Implementation: Develop, test, and deploy machine learning models and geospatial analytics solutions in a production environment. Build and maintain data pipelines that integrate diverse data sources including housing development data, demographic records, migration patterns, land-use plans, and accessibility metrics. Collaborate with engineers and platform teams to ensure models are reliably operationalised and monitored over time.

ML Optimisation and Geospatial Analytics: Develop and refine predictive and spatial models that forecast future education demand across Singapore's planning landscape. Apply techniques such as spatial regression, time-series forecasting, agent-based modelling, or deep learning as appropriate to the problem context. Continuously evaluate model performance, validate outputs against ground truth, and iterate on modelling approaches to improve forecast accuracy and reliability.

Requirements

Experience

• The ideal candidate has strong hands-on experience in data science or a related field, with a demonstrable track record of delivering machine learning solutions in production.

• Prior experience working with geospatial data and tools is strongly preferred, as is experience in domains involving demographic modelling, urban planning, or public sector analytics.

• Familiarity with the Singapore planning context, including URA Master Plan data, HDB housing pipelines, or similar datasets, would be an advantage
Skills

• The candidate should be proficient 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 is a bonus.

• Strong skills in SQL and experience with cloud data platforms (e.g. AWS, GCP, or Azure) are expected.

• The candidate should be familiar with the full ML lifecycle, from data wrangling and feature engineering through to model evaluation, deployment, and monitoring. Candidate should be comfortable with more advanced ML techniques such as ensemble learning, regularisation, agent-based modelling, forecasting, etc.

• Beyond technical skills, the role requires strong communication skills to present findings and recommendations clearly to non-technical stakeholders, and the ability to work collaboratively in a cross-functional team environment.