Manager, RWE Programming/ R Development

Job Summary

Responsibilities

• Provide programming support for RWE project or study to deliver the TLFs on time and support ad-hoc requests.
• Develop and maintain necessary programming macros or tools to effectively support all programming needs.
  • Responsibilities include: identify the macros or tools that will facilitate programing efficiency, support the macro or tool development, and support the macro or tool implementation and maintenance.
• Evaluate, assess and enhance DSI computing environment system with some supervision.
  • Responsibilities include: assess DSI new computing environment system for programming and analysis efficiency, identify system bug/issue and lead the activity to enhance the system, develop system training materials and work as the SME to support implementation, evaluate, request and approve system upgrades, and propose/develop system utilities.
• Provide input to build a strong RWD environment, RWE computing environment, promote innovative solutions, operational excellence and develop efficient processes and standards for RWE.
• Demonstrate high level proficiency for Machine Learning (ML) and Artificial Intelligence (AI) technology, align with future roadmap, network with other business area/analytics group to promote efficiency in the use of AI/ML.

Qualifications

Education Qualifications
Bachelor's Degree from an accredited institution in science or in a technical field preferred Master's Degree preferred

Experience Qualifications
7 or More Years with a Bachelor's Degree or Master’s degree with minimum of 4 years proven experience within pharmaceutical industry, or CROs supporting statistical programming preferred 
4 or More Years Previous experience supporting macro and/or system utility development is highly preferred preferred
Advanced working knowledge of statistical or script programming language such as SAS, R, Python,SQL preferred
Understanding of statistical concepts in support of analyses preferred
Experience in using the RWD including retrospective database, registries, EMR and claims preferred
Knowledge in the secondary database structures or external control arms preferred
Experience in statistical modelling (time to event analysis), causal inference methods (Mixed Models for Repeated Measures, Cox proportional hazard model, propensity scoring techniques, comparative effectiveness analysis, etc.) is preferred preferred