Data Scientist – Geospatial Team

What You Will Be Working On

As a Data Scientist on the Geospatial Team, you’ll be responsible for designing, developing, and maintaining robust solutions that enable analytics and insights across our Client’s long-term infrastructure and space planning and management needs.

One of such projects involves contributing to our Client’s Spatial Modelling Engine, which forecasts future education demand based on:

Housing growth

Demographics

Migration patterns

Land-use plans

Accessibility

Key Responsibilities

1. Requirements Analysis

Work closely with planners, analysts, and stakeholders across our Client to understand long-term infrastructure and space planning needs.

Translate complex business requirements into well-defined analytical and technical specifications.

Conduct exploratory data analysis to surface insights that inform solution design.

Propose scalable, fit-for-purpose approaches that balance analytical rigour with operational practicality.

2. ML Solution Design

Design end-to-end machine learning architectures that support geospatial and demand forecasting use cases, including our Client's Spatial Modelling Engine.

Define:

Data pipelines

Feature engineering strategies

Model serving frameworks

Ensure solutions 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.

3. 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

Accessibility metrics

Collaborate with engineers and platform teams to ensure models are reliably operationalised and monitored over time.

4. 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

Deep learning

Select appropriate techniques based on the problem context.

Continuously evaluate model performance.

Validate outputs against ground truth.

Iterate on modelling approaches to improve forecast accuracy and reliability.

Experience

The ideal candidate has at least 3–5 years of 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.

Experience in domains involving:

Demographic modelling

Urban planning

Public sector analytics is highly advantageous.

Familiarity with the Singapore planning context, including:

URA Master Plan data

HDB housing pipelines

Similar datasets would be an advantage.

Skills

Machine Learning & Data Science

Proficient in Python.

Experience with data science libraries such as:

scikit-learn

PyTorch

TensorFlow

Familiarity with the full machine learning lifecycle, including:

Data wrangling

Feature engineering

Model evaluation

Model deployment

Model monitoring

Comfortable applying advanced machine learning techniques such as:

Ensemble learning

Regularisation

Agent-based modelling

Forecasting

Other advanced ML approaches

Geospatial Technologies

Knowledge of geospatial tools and frameworks such as:

GeoPandas

QGIS

PostGIS

ArcGIS

Experience with geospatial analytics is a bonus.

Data & Cloud Technologies

Strong SQL skills.

Experience with cloud data platforms such as:

AWS

GCP

Azure

Communication & Collaboration

Strong communication skills to present findings and recommendations clearly to non-technical stakeholders.

Ability to work collaboratively in a cross-functional team environment.

Strong analytical thinking and stakeholder engagement capabilities.