Assistant Director, Data Science & Analytics (AI Partnerships and Enablement Lead)

HPB Health Promotion Board

Singapore

5-7 Years

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Posted 3 days ago
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Job Description

[What the role is]

Looking for a job that actually makes a difference
Join us at the Forefront of Shaping Healthier Lives

The Health Promotion Board's vision is to make Singapore a nation of healthier people. Come be a part of this journey if you're passionate about creating boundary-pushing work that drives behavioral change.

Established in 2001, HPB is Singapore's government agency dedicated to empowering citizens towards healthier living through national health promotion programmes that enhance physical and mental well-being across the population.

HPB is advancing its Precision Public Health strategy, delivering personalised, engaging and effective health interventions at scale. Supporting this mission, the Chief Data Officer's Office drives data-informed decision-making across the organisation, overseeing HPB's data strategy, governance, engineering and analytics capabilities to ensure responsible and effective data use.

CDOO comprises four key functions: Planning & Integration, Data Governance & Compliance, Data Engineering & Architecture, and Data Science & Analytics.

The Data Science & Analytics department transforms individual-level data into actionable insights that support HPB's Precision Public Health goals. DSA strengthens analytics rigour and expands data science applications, enabling personalised interventions and targeted messaging to drive sustained behavioural change through sophisticated data integration across diverse programmes.

DSA partners extensively with GovTech, academia, health clusters, AI Singapore, A.STAR and other collaborators to develop predictive models, deploy AI solutions and implement advanced analytics on platforms like the Healthy 365 app.

In the rapidly evolving AI landscape, DSA co-oversees HPB's AI Governance Working Committee, operationalising Board-wide AI governance by translating whole-of-government Generative AI guidelines into HPB's context and harmonising governance practices across AI projects.

[What you will be working on]

You will lead the AI Partnerships and Enablementteam -shaping strategic collaborations and governance implementation across HPB:

AI partnerships:

Engage and manage collaborations with public healthcare institutions, data scienceorganisations,academiaand technology partners.

Co-develop and deploy AI models alongside partners.

Bridge external partners and internal business teams to ensure effective translation of data-driven insights into operational use cases.

Work with diverse, large-scale datasets (e.g.wearable sensor data, meal logging, sports participation data) to enable feature engineering and AI applications thatbenefitcitizens.

AI governance:

Shape and implement HPB's AI governance roadmap across the AI lifecycle - fromconceptualisationto deployment.

Embed governance standards to ensure AI systems are safe,reliableand high-quality.

Uphold internalstakeholderand citizen trust through responsible AI practices.You will also shape the implementation roadmap of HPB's AI governance standards and practices, to uphold internal stakeholder and citizen trust in deploying AI models that are safe, reliable and of high quality.

[What we are looking for]

An ideal candidate should have a Bachelor's, Master's or PhD in AI, Data Science, Analytics, Computer Science, Computer Engineering or Information Systems, with major or project experience in data science or business analytics from a recognised university. Professional certifications in Data Science, Machine Learning Engineering or Business Analytics from accredited bodies, coupled with relevant working experience, will also be considered for candidates from other disciplines.

You should have minimum 5 years of experience managing data science projects, including implementation of responsible AI practices. Experience in end-to-end deployment of machine learning solutions in production environments and working with cloud-based analytics platforms like Microsoft Azure, Hadoop and Spark ecosystems to productionise ML solutions would be advantageous.

Technical requirements include familiarity with developing machine learning and deep learning models using programming languages such as Python, SQL, R and PySpark.

You must be adaptive in fast-paced matrix environments, overseeing multiple projects concurrently in agile settings.