Singapore, Singapore
Onsite
[What the role is]
The Health Promotion Board’s vision is to make Singapore a nation of healthier people.[What you will be working on]
As a member of the Analytics and Translation team in the Research, Evaluation and Monitoring division, you will work in a cross-functional, matrix team to contribute to high-impact projects in priority growth areas.
You will generate actionable insights through advanced analytics on diverse datasets (e.g. large longitudinal cohorts, sensors, and combined datasets), applying appropriate statistical and machine learning approaches to support risk prediction, population segmentation, and identification of behavioural patterns.
You will support the development and application of predictive models and segmentation approaches (e.g. clustering) to inform programme targeting, intervention design, and risk stratification, ensuring outputs are robust, interpretable, and relevant for real-world decision-making.
You will set up and support behavioural experiments and programme evaluations, including contributing to study design, data preparation, and analysis to distil research insights and evaluate suitability for translation into programme delivery.
You will develop compelling data-driven narratives for impactful storytelling to both internal and external stakeholders, translating technical findings into clear, concise, and actionable insights that inform programme and policy decisions.
You will work with cross-functional teams and external partners to support collaborations and analytics projects, contributing to delivery of strategic outcomes and facilitating knowledge sharing and learning across teams.
You will support the development of data pipelines, feature engineering, and preparation of datasets to enable efficient and scalable analytics, including working with relevant data platforms where required.
[What we are looking for]
The ideal candidate will be a dynamic and self-motivated individual with a strong interest in advancing health promotion through data, research, and analytics. The candidate should be comfortable working at the intersection of data science and real-world application, and is motivated by translating insights into impact at scale.
The candidate should also possess:
Degree in statistics, data analytics, social sciences, life sciences, public health, or any related quantitative discipline
At least 2–6 years of practical experience working with data as an analyst or in a related role
Experience in applying statistical techniques and/or machine learning methods (e.g. regression, clustering, predictive modelling) to real-world datasets
Proficiency in at least one programming language (e.g. R, Python). Familiarity with Databricks, SparkR, PySpark or working with large datasets will be advantageous
Additional familiarity with statistical software (e.g. SPSS, STATA) will be a bonus
Familiarity with programme evaluation methods or quasi-experimental study designs will be advantageous
Strong communication skills, especially in communicating technical findings to a non-technical audience
Strong project management skills and ability to manage multiple priorities in a fast-paced environment
Comfortable working independently and as part of a team
Strong sense of intellectual curiosity and willingness to learn and seek continuous improvement
Adaptable, flexible, and able to take initiative and prioritise among competing demands
Successful candidates will be offered a 2-year contract in the first instance.
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