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National University of Singapore

4.0

Research Associate (Comparative Effectiveness and Real-World Data Analytics)

Queenstown Estate

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Job Title: Research Associate (Comparative Effectiveness and Real-World Data Analytics)

University-Level Unit: Saw Swee Hock School of Public Health

Faculty/Department-Level Unit: Saw Swee Hock School of Public Health

Employee Category: Research Staff

Location_ONB: Kent Ridge Campus

Posting Start Date: 16/04/2026

Job Description

Applications are invited for the following full-time position in the Saw Swee Hock School of Public Health:

Research Associate (Comparative Effectiveness and Real-World Data Analytics)

Position Summary
The Centre for Health Intervention and Policy Evaluation Research (HIPER) ( https://hiper.nus.edu.sg/) at the Saw Swee Hock School of Public Health (SSHSPH), NUS, is seeking to hire a researcher with a relevant Master’s degree to lead Phase III of the RODEO project, focusing on comparative analyses using oncology real-world data. The candidate will be responsible for designing and conducting comparative effectiveness studies across multiple myeloma, non-small cell lung cancer, and ovarian cancer using structured and processed unstructured data from electronic medical records.

The role is particularly suited to candidates with training in biostatistics, epidemiology, health services research, health economics, public health, data science, or a related field, and who have experience working with observational healthcare datasets.

Key Responsibilities:

Lead the design and execution of comparative effectiveness analyses using real-world oncology data
Develop study protocols and statistical analysis plans for observational cohort analyses
Apply appropriate methods to address confounding, selection bias, missing data, and other threats to internal validity in real-world studies
Conduct analyses using methods such as propensity score matching, inverse probability weighting, direct covariate adjustment, difference-in-differences, instrumental variables, and survival analysis
Perform time-to-event analyses, including overall survival and progression-related outcomes
Support partitioned survival analyses and other methods relevant to economic evaluation and health technology assessment
Work closely with clinicians, health economists, and data scientists to identify clinically relevant treatment comparisons and confounders
Collaborate with data engineers and analysts to ensure appropriate variable construction and dataset readiness
Interpret findings in the context of health technology assessment, reimbursement, and policy decision-making
Prepare technical reports, presentations, manuscripts, and conference abstracts
Ensure analyses adhere to methodological standards such as STROBE and ISPOR good practice recommendations for real-world evidence studies
Participate in project meetings with collaborators across NUS, NCIS, NUH, TTSH, NCCS, and other partner institutions

Requirements:

Experience working with large observational healthcare datasets, registries, or electronic medical records
Strong knowledge of causal inference and comparative effectiveness methods
Experience using statistical software such as R, Stata, SAS, or Python
Familiarity with oncology data, survival analysis, and longitudinal data analysis will be an advantage
Understanding of health technology assessment, outcomes research, or economic evaluation is desirable
Strong written and verbal communication skills
Ability to work independently while collaborating effectively in a multidisciplinary research team

Preferred Attributes:

Prior experience with real-world evidence generation in oncology
Familiarity with OMOP common data model, OHDSI tools, or TRUST platform datasets
Experience translating statistical findings into policy-relevant insights for clinicians, hospital leaders, or HTA agencies
Interest in contributing to publications and future grant applications

Qualifications

Master’s degree in Biostatistics, Epidemiology, Public Health, Health Economics, Data Science, Statistics, Health Services Research, or a related discipline

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National University of Singapore

4.0

Research Associate (Comparative Effectiveness and Real-World Data Analytics)

Queenstown Estate

National University of Singapore

4.0

Research Associate (Comparative Effectiveness and Real-World Data Analytics)

Queenstown Estate

Is your resume a good match?

Use AI to find out how well the skills on your resume fit this job description.

Job Title: Research Associate (Comparative Effectiveness and Real-World Data Analytics)

University-Level Unit: Saw Swee Hock School of Public Health

Faculty/Department-Level Unit: Saw Swee Hock School of Public Health

Employee Category: Research Staff

Location_ONB: Kent Ridge Campus

Posting Start Date: 16/04/2026

Job Description

Applications are invited for the following full-time position in the Saw Swee Hock School of Public Health:

Research Associate (Comparative Effectiveness and Real-World Data Analytics)

Key Responsibilities:

Lead the design and execution of comparative effectiveness analyses using real-world oncology data
Develop study protocols and statistical analysis plans for observational cohort analyses
Apply appropriate methods to address confounding, selection bias, missing data, and other threats to internal validity in real-world studies
Conduct analyses using methods such as propensity score matching, inverse probability weighting, direct covariate adjustment, difference-in-differences, instrumental variables, and survival analysis
Perform time-to-event analyses, including overall survival and progression-related outcomes
Support partitioned survival analyses and other methods relevant to economic evaluation and health technology assessment
Work closely with clinicians, health economists, and data scientists to identify clinically relevant treatment comparisons and confounders
Collaborate with data engineers and analysts to ensure appropriate variable construction and dataset readiness
Interpret findings in the context of health technology assessment, reimbursement, and policy decision-making
Prepare technical reports, presentations, manuscripts, and conference abstracts
Ensure analyses adhere to methodological standards such as STROBE and ISPOR good practice recommendations for real-world evidence studies
Participate in project meetings with collaborators across NUS, NCIS, NUH, TTSH, NCCS, and other partner institutions

Requirements:

Experience working with large observational healthcare datasets, registries, or electronic medical records
Strong knowledge of causal inference and comparative effectiveness methods
Experience using statistical software such as R, Stata, SAS, or Python
Familiarity with oncology data, survival analysis, and longitudinal data analysis will be an advantage
Understanding of health technology assessment, outcomes research, or economic evaluation is desirable
Strong written and verbal communication skills
Ability to work independently while collaborating effectively in a multidisciplinary research team

Preferred Attributes:

Prior experience with real-world evidence generation in oncology
Familiarity with OMOP common data model, OHDSI tools, or TRUST platform datasets
Experience translating statistical findings into policy-relevant insights for clinicians, hospital leaders, or HTA agencies
Interest in contributing to publications and future grant applications

Qualifications

Master’s degree in Biostatistics, Epidemiology, Public Health, Health Economics, Data Science, Statistics, Health Services Research, or a related discipline

See company reviews

Working here doesn’t have to be a secret

4.0

Recommend to a friend
Approve of CEO
CEO: Tan Eng Chye
1,122 Ratings

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Working here doesn’t have to be a secret