Job Description

[What the role is]

As Assistant Director (Biostatistics and Modelling) in the Advanced Methods and Analytics Division, you will provide scientific and operational leadership to a multidisciplinary team of biostatisticians, modellers, and data scientists. You will set the technical direction and scientific standards for the division's analytical work across outbreak analytics, infectious disease modelling, and disease forecasting. Your team's outputs will directly inform Singapore's communicable disease policy, outbreak response decisions, and long-term public health strategy making scientific credibility, policy fluency, and leadership capability equally essential to this role.

[What you will be working on]

You will lead the team in conceptualising, designing, and executing advanced analytical and modelling work, drawing on a sophisticated repertoire of epidemiological and statistical methods, mathematical modelling, AI and machine learning approaches, and health economic frameworks. You will set the strategic direction for the team's methodological development, critically interrogate existing approaches, and drive the adoption of state-of-the-art methods where standard approaches are insufficient.

An important part of your role is to ensure the team can deliver timely, credible analytical support during public health emergencies, including the rapid delivery of analytical outputs that can be stood up at pace during a crisis.

You will also be responsible for the day-to-day leadership and operations of the team, including workforce planning and professional development, and will represent the division in senior inter-agency engagements and international technical forums.

[What we are looking for]

Key job responsibilities:

• Provide scientific and operational leadership to the Biostatistics and Modelling team, setting technical direction and fostering a culture of rigour, innovation, and collaborative problem solving
• Lead the development and application of advanced analytical methods for communicable disease surveillance and outbreak investigation, including cluster detection, transmission dynamics, risk factor analyses, and real-time estimation of epidemiological parameters such as reproduction numbers and growth rates
• Oversee the development, validation, and application of mathematical and computational models including dynamic transmission models and agent-based models to assess disease trajectories, evaluate intervention impact, and generate scenario-based projections for operational and strategic decision making
• Direct the development of disease forecasting models integrating epidemiological and other relevant data streams to generate short- and medium-term predictions of disease activity, supporting early warning, resource planning, and pre-emptive public health action, with rigorous frameworks for forecast evaluation and uncertainty quantification
• Drive the continuous advancement of the division's methodological capabilities, identifying opportunities to adopt or develop state-of-the-art approaches while maintaining the scientific rigour and reproducibility that high-stakes policy work demands
• Engage with academic and scientific communities, including through contributions to peer-reviewed publications and/or participation in technical working groups
• Prepare and present technically sophisticated outputs to senior stakeholders and expert audiences, translating complex findings into clear policy implication.

Job requirements:

• Postgraduate qualification, preferably at doctoral level, in epidemiology, biostatistics, infectious disease modelling, health economics, public health, data science or a closely related quantitative discipline; candidates with a master's degree and an exceptional track record of applied technical leadership will be considered.
• Minimum 8 years of relevant experience in public health, biostatistics, epidemiology, infectious disease modelling, or a related field, with at least 2 years in a leadership or supervisory capacity.
• Demonstrated experience leading complex, high-stakes analytical work in a policy-relevant or operational public health setting, including during outbreak or emergency response contexts.
• Technical expertise across multiple methodological domains, including infectious disease modelling, biostatistics, health economic evaluation, and/or AI or machine learning methods applied to public health data.
• Proven ability to translate complex technical findings into clear, credible, and actionable recommendations for senior policy makers and non-technical audiences.
• Strong stakeholder management experience, including the ability to engage and influence across agencies and with international technical partners.