Research Assistant (Urban Studies / Quantitative Focus)

NATIONAL UNIVERSITY OF SINGAPORE; Singapore; On-site; SGD 50,000 - 70,000; Full time

Job summary

A leading academic institution in Singapore is seeking a highly motivated Research Assistant for the SGHA-TGM project. The role involves developing computational models from large datasets to support urban planners in understanding household preferences. Candidates should have a relevant degree and skills in Python or R, as well as experience in clustering and classification techniques. This position focuses on data science applications in social and urban contexts and offers an opportunity to work collaboratively across disciplines.

Qualifications

• Bachelor’s or Master’s Degree in related quantitative disciplines.
• Proficient in Python and/or R with data manipulation capabilities.
• Experience with clustering, classification, and supervised ML techniques.

Responsibilities

• Clean and integrate large population datasets from multiple sources.
• Implement clustering, classification, and probabilistic modeling techniques.
• Prototype and refine different models and algorithms.

Skills

Data manipulation and analysis in Python/R

Clustering techniques

Classification techniques

Machine Learning libraries (sklearn, PyTorch)

Good communication skills

Education

Bachelor’s or Master’s Degree in Data Science, Statistics, Computer Science, Urban Analytics

Tools

SPSS

SAS

Job description

Interested applicants are invited to apply directly at the NUS CareerPortal

Your application will be processed only if you apply via NUS CareerPortal

We regret that only shortlisted candidates will be notified.

Job Description

• The Singapore Household Archetyping & Town Geodemographic Modelling (SGHA-TGM) project, commissioned by the Ministry of National Development, aims to build foundational knowledge in Singapore population geodemographics, with a particular emphasis on understanding household lifestyle preferences and their relationships with the built environment. The project is intended to develop the knowledge and technical capabilities to surface insights into the diverse socio-spatial contexts of urban living in Singapore, to support urban planners in anticipating evolving needs, sensemaking sentiments on the ground, and fostering a more inclusive and liveable urban environment.
• We seek a highly motivated Research Assistant to contribute to the quantitative components of the SGHA-TGM project. You will develop computational models to enable the discovery of latent population structures, archetypes, and typologies from large-scale datasets. You will work alongside qualitative researchers to interpret and evolve the representational fidelity of groupings and insights derived from data to connect to lived experience and reflect real diversity in urban social life. These outputs will support forecasting frameworks and help planners anticipate evolving household needs at finer spatial scales.

Responsibilities

• Clean, process, and integrate large population datasets from multiple sources (demographics, survey, behavioral, or administrative data), with differing schemas and granularity.
• Implement clustering, classification, and probabilistic modeling techniques (such as statistical matching, spatial disaggregation, generative models) to derive household/resident archetypes from heterogeneous data sources.
• Prototype, test, and refine different models/algorithms.
• Collaborate with social science & urban studies researchers to ground quantitative findings in real-world population dynamics.
• Coordinate with other technical teams to align modeling outputs with downstream projection needs and improve end-to-end workflows.
• Contribute to data/method documentation, visualisations, and writing reports/publications.

Qualifications

Requirements

• Bachelor’s or Master’s Degree in Data Science, Statistics, Computer Science, Urban Analytics, or related quantitative disciplines.
• Proficiency in Python and/or R, including data manipulation and analysis packages (e.g. SPSS/SAS).
• Experience with clustering, classification, and supervised ML techniques, and relevant ML libraries (e.g., sklearn, PyTorch)
• Familiarity with working on large and multi-source datasets; both structured and unstructured.
• Resourceful and critical with good communication skills; able to work independently while collaborating effectively with interdisciplinary teams of social scientist and planners.

Preferred

• Experience working with population data (e.g., census), as well as behavioural (e.g., travel survey, choice experiments) and/or spatial data structures.
• Familiarity with probabilistic models, generative models, graphical models, and/or deep learning.
• Familiarity with data fusion techniques such as statistical matching and/or marginal fitting.
• Interest in social/urban applications of data science.