Data Scientist

Key Responsibilities: Requirements Analysis: Work closely with planners, analysts, and stakeholders across clients 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 client'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 modeloutputs 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 operationalized 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 reliabilityQualifications: At least 3–5 years of hands-on experience in data science or a related field.Demonstrable track record of delivering machine learning solutions in production.Prior experience working with geospatial data and tools is strongly preferred.Experience in demographic modelling, urban planning, or public sector analytics is preferred.Familiarity with the Singapore planning context, including URA Master Plan data, HDB housing pipelines, or similar datasets, is an advantage.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 SQL skills.Experience with cloud data platforms (e.g. AWS, GCP, or Azure).Familiar with the full ML lifecycle, including data wrangling, feature engineering, model evaluation, deployment, and monitoring.Comfortable with advanced ML techniques such as ensemble learning, regularisation, agent-based modelling, forecasting, etc.Strong communication skills to present findings and recommendations clearly to non-technical stakeholders.Ability to work collaboratively in a cross-functional team environment.