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Real World Evidence Data Analyst
2 weeks ago
Knowledge and expertise are empowering leaders across the life sciences to make better data-driven decisions.
We deliver cutting-edge analytics from a broad set of data sources, combined with deep technical and domain expertise. Our data analytics and software platforms support data science, commercial operations, real-world evidence, and cloud information management.
Job Overview:
Seeking an experienced Health Economics Associate responsible for scientific and methodological aspects of observational database analytics and evidence synthesis with minimal supervision.
- Consult on internal RWE databases studies/analyses including objectives, study design, data source identification, protocol development, statistical analysis, and interpretation.
- Execute RWE studies/analyses by SAS based on 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 stakeholders are aware of project status.
- Communicate research findings internally and externally, as appropriate.
- Implement latest methodologies and analytical techniques.
- Contribute to the continuing education of relevant line functions on RWE methodologies.
Key Qualifications:
Positive attitude: Good candidates care about the result and how it is used.
- Good problem-solving/critical thinking skills: Good candidates understand problems clearly and propose solutions in a coherent manner.
- Solid programming and analytical skills: Good candidates demonstrate competence in statistical techniques and professional programming habits.
- Good communication skills: Good candidates can communicate study results in a clear, non-technical manner.
- Great at time and task management: Good candidates estimate task completion time and deliver on time.
- Quick learning capability/Self-motivated: Good candidates learn new technologies on their own.
- Good team player.
Technical Requirements:
- Preferred MS in statistics, biostatistics, epidemiology, computer science, or other quantitative analysis fields.
- Demonstrated competence in statistical techniques, such as hypothesis testing, regression analysis, machine learning, etc.
- Professional programming skills with SAS and SQL. R and/or Python programming experience is plus.
