Real World Evidence Data Scientist

This position is responsible for the scientific and methodological aspects of all observational database analytics and evidence synthesis with minimal supervision.

Our company delivers cutting-edge analytics from the broadest set of data sources, combined with deep technical and domain expertise. We optimize business strategy and operations by delivering analytical guidance to the field through our cloud-based sales planning and operations platform.

• Provide consultation on all aspects of 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 all stakeholders aware of project status.
• Effectively communicate research findings internally and externally, as appropriate (e.g., congresses, publications).
• Ensure latest methodologies and analytical techniques are implemented.
• Contribute to the continuing education of relevant line functions on RWE methodologies.

• Minimum MS in statistics, biostatistics, epidemiology, health economics and outcomes research, health policy, or similar.
• At least 2 years' hands-on experience in observational data studies.
• Professional programming skills with SAS. SQL and R programming experience are plus.
• Excellent interpersonal communication and study management skills.
• Ability to take detailed HE&OR study results and communicate them in a clear, non-technical manner to internal cross-functional teams, using language that resonates with the teams, while maintaining the integrity of key findings.
• Ability to work effectively in a constantly changing, diverse, and matrix environment.
• Ability to proactively identify new opportunities and solutions
• Strong working knowledge of the Microsoft Office Suite (Word, PowerPoint, Excel)

• Positive attitude: Good candidates care about the result and how the result be used.
• Good problem-solving/critical thinking skills: Good candidates are able to understand problems clearly.
• Solid programming and analytical skills: Good candidates demonstrate competence in the application of statistical techniques and professional in programming which includes good programming habit and self-QC process.
• Good communication skills: Good candidates are able to take detailed study results and communicate them in a clear, non-technical manner to internal cross-functional teams, using language that resonates with the teams, while maintaining the integrity of key findings.
• Great at time and task management: Good candidates are able to estimate the amount of time needed to complete a task, communicate this to client clearly and deliver it on time.
• Quick learning capability/Self-motivated: Good candidates have the ability to learn new technologies on their own.
• Good team player

• Preferred MS 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..
• Professional programming skills with SAS and SQL. R and/or Python programming experience is plus.
• Strong working knowledge of the Microsoft Office Suite (Word, PowerPoint, Excel).