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全职 Research Associate (Comparative Effectiveness and Real-World Data Analytics) 工作, 薪水, National University Of Singapore Central Region (Singapore) 公司招聘中 - Ricebowl

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

National University Of Singapore

Undisclosed

全职

Queenstown, Central Region (Singapore)

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工作地点

  • Queenstown Central Region (Singapore) Singapore

职位描述

岗位职责

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) ( *************) 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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