Director, Statistical Programming

BioNTechCambridge, MA
95d

About The Position

Responsibilities • Partner with Head of Statistical Programming to define and execute a comprehensive programming strategy, including vendor oversight, process automation, and adherence to industry standards. • Lead and oversee a team of internal/FSP programmers and CROs to ensure timely, high-quality delivery of analysis datasets, tables, listings, and figures (TLFs). • Drive the creation, review, and validation of SAS/R programs for SDTM/ADaM datasets, efficacy/safety outputs, and integrated summaries, ensuring reproducibility and compliance with SOPs and regulatory standards. • Collaborate with Biostatistics, Clinical Development, Data Management, and Regulatory Affairs to influence study designs, statistical analysis plans, and submission strategies. • Lead the programming contribution to global regulatory submissions (NDA, BLA, MAA), including submission ready datasets, TLFs, define.xml, and reviews’ guide. • Champion adoption of advanced analytics, automation, and emerging technologies (e.g., R, Python, AI/ML) to optimize workflows and mentor teams on industry innovations. • Establish and maintain robust programming processes, infrastructure, and SOPs to enhance efficiency and standardization across studies and submissions. • Contribute to continuous improvement and global clinical initiatives to strengthen BioNTech’s clinical operation and data analysis capabilities. Qualifications Education • Bachelor’s degree in Statistics, Mathematics, Computer Science or related discipline, advanced degree preferred Experience • 15+ years (10+ years for advanced degree) experience in a pharmaceutical industry, CRO or another clinical research setting, with a focus on oncology. • Expert knowledge of statistical programming in SAS (Base, Macro, STAT, GRAPH, SQL). • Solid understanding of FDA, EMA, ICH, and global regulations and guidelines. • Deep knowledge of clinical study data standards and reporting requirements, including CDISC (SDTM and ADaM). • Thorough understanding of the drug development process across early- to late-stage development and submission. • Demonstrated expertise in supporting electronic submissions (eCDT, define.xml, reviewer’s guides). • Proven project management skills with the ability to oversee multiple concurrent projects and global vendors.

Requirements

  • 15+ years (10+ years for advanced degree) experience in a pharmaceutical industry, CRO or another clinical research setting, with a focus on oncology.
  • Expert knowledge of statistical programming in SAS (Base, Macro, STAT, GRAPH, SQL).
  • Solid understanding of FDA, EMA, ICH, and global regulations and guidelines.
  • Deep knowledge of clinical study data standards and reporting requirements, including CDISC (SDTM and ADaM).
  • Thorough understanding of the drug development process across early- to late-stage development and submission.
  • Demonstrated expertise in supporting electronic submissions (eCDT, define.xml, reviewer’s guides).
  • Proven project management skills with the ability to oversee multiple concurrent projects and global vendors.

Responsibilities

  • Partner with Head of Statistical Programming to define and execute a comprehensive programming strategy, including vendor oversight, process automation, and adherence to industry standards.
  • Lead and oversee a team of internal/FSP programmers and CROs to ensure timely, high-quality delivery of analysis datasets, tables, listings, and figures (TLFs).
  • Drive the creation, review, and validation of SAS/R programs for SDTM/ADaM datasets, efficacy/safety outputs, and integrated summaries, ensuring reproducibility and compliance with SOPs and regulatory standards.
  • Collaborate with Biostatistics, Clinical Development, Data Management, and Regulatory Affairs to influence study designs, statistical analysis plans, and submission strategies.
  • Lead the programming contribution to global regulatory submissions (NDA, BLA, MAA), including submission ready datasets, TLFs, define.xml, and reviews’ guide.
  • Champion adoption of advanced analytics, automation, and emerging technologies (e.g., R, Python, AI/ML) to optimize workflows and mentor teams on industry innovations.
  • Establish and maintain robust programming processes, infrastructure, and SOPs to enhance efficiency and standardization across studies and submissions.
  • Contribute to continuous improvement and global clinical initiatives to strengthen BioNTech’s clinical operation and data analysis capabilities.
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