Data Scientist (Geospatial)

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.RequirementsExperience • 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 advantageSkills • 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.

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.

Unison Consulting was launched in Singapore on September 2012, the hub of the financial industry, with innovative visions in the technocratic arena. We are a boutique next-generation Technology Company with strong business-interests in Liquidity risk, Market Risk, Credit Risk and Regulatory Compliance. Unison provides technology consulting and services to implement Risk Management and Risk Analytics System for Financial Institutions. Our services suite comprises of Techno-Functional consulting, systems integration, Business Intelligence, information management, and custom development of IT solutions, plus project management expertise for financial institutions. We have expertise in latest cutting edge technology to achieve better total cost of ownership. Through our qualified professionals, we assist you drive your unique risk management strategies, whether that means efficient monitoring, improving risk appetite of the financial institutions, complying with regulations, or capturing growth opportunities through innovation, this is what maximizes your decision taking potential. At Unison Consulting, we view clients as partners, and our success is only measured by the success of our partners. So we put it all on the table in order to exceed expectations. Our staff consists of young, energetic and innovative consultants who are never afraid to challenge the conventions and push the boundaries in an effort to help our clients. For every project, no matter how large or how small, we strive to not only meet your needs, but deliver a showcase in your field.

Unison Consulting was launched in Singapore on September 2012, the hub of the financial industry, with innovative visions in the technocratic arena. We are a boutique next-generation Technology Company with strong business-interests in Liquidity risk, Market Risk, Credit Risk and Regulatory Compliance. Unison provides technology consulting and services to implement Risk Management and Risk Analytics System for Financial Institutions.

Our services suite comprises of Techno-Functional consulting, systems integration, Business Intelligence, information management, and custom development of IT solutions, plus project management expertise for financial institutions. We have expertise in latest cutting edge technology to achieve better total cost of ownership. Through our qualified professionals, we assist you drive your unique risk management strategies, whether that means efficient monitoring, improving risk appetite of the financial institutions, complying with regulations, or capturing growth opportunities through innovation, this is what maximizes your decision taking potential.

At Unison Consulting, we view clients as partners, and our success is only measured by the success of our partners. So we put it all on the table in order to exceed expectations. Our staff consists of young, energetic and innovative consultants who are never afraid to challenge the conventions and push the boundaries in an effort to help our clients. For every project, no matter how large or how small, we strive to not only meet your needs, but deliver a showcase in your field.