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 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 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 reliability

Qualifications: 

• 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.