Research Associate (HSRPH/SS)

Duke-NUS Medical School; Singapore; Presencial; SGD 42.000 - 70.000; Jornada completa

Descripción de la vacante

Duke-NUS Medical School seeks a data-oriented professional to support the Health Services Research & Population Health (HSRPH) programme. The role involves data cleaning, analysis of quantitative data, and assisting in preparing abstracts and manuscripts for dissemination.

You will help prepare presentations for diverse stakeholders and conduct literature reviews to assess the potential value of health innovations.

Formación

• Bachelor’s degree in any discipline; training in statistics, economics, data science, public health, epidemiology or related fields preferred.
• Minimum 2 years of working experience with quantitative data.
• Adept at working with data, processing raw data to visualise trends and patterns.

Responsabilidades

• Perform data cleaning, handling and analysis.
• Assist in drafting research abstracts and manuscripts.
• Prepare PowerPoint presentations for stakeholders.
• Conduct literature reviews to evaluate value of health innovations.
• Assist in preparation of evaluation reports for the health economics team.
• Extract data from databases and published literature.
• Support project management across stages: objectives, design, data extraction, data collation, analysis, modelling, stakeholder engagement under guidance.
• Support secretariat and administrative functions for the health economics team.

Conocimientos

Quantitative skills

Data handling

R programming

Communication skills

Educación

Bachelor’s degree in any discipline

Herramientas

R

Descripción del empleo

Job Description

Duke-NUS embodies a strategic partnership between Duke University and National University of Singapore (NUS). It works closely with the Singapore Health Services (SingHealth) cluster which is a network of national specialty disease centres, hospitals and polyclinics, to advance medicine and improve lives through cutting-edge research and education. Together, Duke-NUS and SingHealth form an Academic Medical Centre that comprises a number of academic collaborations.

The Signature Research Programme in Health Services Research & Population Health (“HSRPH”) in Duke-NUS Medical School is a centre of academic excellence for research and education of health systems and services for the population in Singapore and beyond. Our research methods include implementation science, health economics, decision science, survey/qualitative research, quantitative medicine, epidemiology, and data science. The population health outcomes programme Duke-NUS has established provides a framework for evaluating and improving healthcare delivery systems nationwide. We are also developing a range of educational activities for healthcare professionals including short courses, graduate certificates, Masters course and PhD programmes.

Job Description

• Perform data cleaning, handling and analysis
• Assist in drafting research abstracts and manuscripts
• Assist in preparation of PowerPoint presentations to various stakeholders
• Conduct literature reviews to evaluate the potential value of health innovations
• Assist in preparation of evaluation reports for the health economics team
• Extract data from existing databases and published literature
• Support project management activities across different stages of research, including development of study objectives, study design, data extraction, data collation, statistical analysis, modelling, and stakeholder engagement, under guidance from senior team members.
• Support secretariat and administrative functions pertaining to the health economics team at HSRPH

Qualifications

Job Requirements

• Bachelor’s Degree in any discipline. Candidate with training in statistics, economics, data science, public health, epidemiology, or related disciplines, and with strong quantitative skills is preferred.
• Minimum 2 years of working experiences with quantitative data
• Adept at working with data, including processing raw data to visualise trends and deciphering patterns and ability to handle large data sets
• Experience in various quantitative research methods is preferred.
• Experience with R programming language, and willing to learn more applications
• Excellent verbal and written communication and interpersonal skills
• Able to work in a team, with dynamic reporting lines and multiple stakeholders
• Able to work independently with limited supervision
• Individual who is action-oriented, highly motivated and target-focused

We regret that only shortlisted candidates will be notified.