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Lead and oversee the application of biostatistical methods to analyze and interpret clinical trial data across multiple studies or programs, ensuring scientific rigor and consistency
Provide strategic direction and oversight for statistical programming using R and Python, enabling scalable, efficient, and compliant data analysis and reporting solutions
Manage and review the development of tables, listings, and figures (TLFs), as well as outputs supporting clinical study reports, publications, and regulatory submissions
Ensure compliance with regulatory requirements, industry standards (including CDISC SDTM and ADaM), internal quality processes, and support audits and inspections as needed
Collaborate with cross-functional teams to ensure timely delivery of study outputs
Identify, prioritize, and implement process improvements, tools, and innovative technologies to advance statistical programming and reporting capabilities
Stay current with emerging methodologies, technologies, and regulatory guidelines, and incorporate best practices into team workflows
Provide leadership, mentorship, and technical guidance to statisticians and programmers, fostering a culture of continuous improvement and excellence
Assist in developing and implementing AI-enabled technologies to streamline statistical workflows and build automated, reproducible reporting pipelines that enhance efficiency, quality, and traceability
Manage self and other resources according to priorities
Knowledgeable of developments in the field of statistics in drug development
Contribute to the review and maintenance of Amgen policies, SOPs, and controlled documents to ensure ongoing compliance and alignment with regulatory standards
Requirements
MSc or PhD in Statistics, Biostatistics, or related field
8 years relevant industry experience
Proven ability to apply statistical methods in clinical development
Strong programming skills in R and/or Python
Experience with R Shiny or interactive reporting tools
Nice to have
Doctorate degree in Statistics/Biostatistics
Experience in machine learning, real world data analytics, electronic health record data analysis, clinical trial simulation or related applications
Life-cycle drug development experience
Experience working effectively in a globally dispersed team environment with cross-cultural partners