Health Economist

This is a challenging role that involves utilizing scientific and methodological expertise to design, execute and interpret studies using observational database analytics. The ideal candidate will have a strong background in health economics, statistics or a related field, and excellent programming skills in SAS, SQL, and R/Python.

• Provide consultation on internal RWE databases studies/analyses, including objectives, study design, data source identification, protocol development, statistical analysis, and interpretation.
• Execute RWE studies/analyses using claim database, EMR database, registry data, and public used files.
• Generate high-quality, readily interpretable deliverables (e.g., data tables, graphs, charts, study reports).
• Develop mock table shells, prepare specifications, and ensure quality control on analyses conducted by junior programmers.
• Effectively manage and track all studies/analyses to ensure timelines are met and all stakeholders are aware of project status.
• Communicate research findings internally and externally, as appropriate (e.g., congresses, publications).
• Implement latest methodologies and analytical techniques.
• Contribute to the continuing education of relevant line functions on RWE methodologies.

• Mastery in statistics, biostatistics, epidemiology, health economics, outcomes research, health policy, or similar field.
• At least 2 years' hands-on experience in observational data studies.
• Strong programming skills in SAS, SQL, and R/Python.
• Excellent interpersonal communication and project management skills.
• Ability to communicate complex study results in a clear, non-technical manner to internal cross-functional teams.
• Ability to work effectively in a constantly changing, diverse, and matrix environment.
• Familiarity with Microsoft Office Suite (Word, PowerPoint, Excel).

• Positive attitude and willingness to take ownership of responsibilities.
• Good problem-solving and critical thinking skills.
• Solid programming and analytical skills.
• Effective communication and presentation skills.
• Excellent time and task management skills.
• Quick learning capability and self-motivation.
• Good team player.

• Preferred Master's degree in statistics, biostatistics, epidemiology, computer science, or other quantitative analysis fields.
• Demonstrated competence in the application of statistical techniques, such as hypothesis testing, regression analysis, machine learning, etc.
• Proficiency in programming languages SAS, SQL, and R/Python.
• Strong knowledge of Microsoft Office Suite (Word, PowerPoint, Excel).