Role Summary
Director Statistical Programming (Oncology) overseeing the development and execution of programming strategy, team leadership across internal, FSP, and CRO partners, and regulatory submission support. Locations include Cambridge, US; Gaithersburg, US; New Jersey, US. Full-time role focused on delivering high-quality analysis datasets, TLFs, and submission-ready outputs in oncology programs.
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
- 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.
Education
- Bachelor’s degree in Statistics, Mathematics, Computer Science or related discipline, advanced degree preferred