Paid / Remote / Full-time
The company is working in the field of neurodegeneration which is a very exciting and unexplored field. This means a lot of opportunity for implementing new ideas and gain an in depth understanding of how data analysis can be done in biotech with latest toolsets.
Tools: R, Shiny, Markdown; working knowledge of Git and Unix/Linux environments.
- Devise and lead statistical strategies for drug candidates' clinical development, including pivotal Phase 2/3 studies and NDA/MAA/BLA submissions.
- Ensure statistical integrity of the analysis and reporting deliverables, including guidance on statistical methodology, optimization of study designs, endpoint selection, author/review statistical sections of the protocol, clinical study reports and related documents, oversee timelines, and study level tasks regarding statistical analysis and reporting.
- Conduct hands-on exploratory analyses, sample size estimation, modeling and simulation, and create tools to gain valuable insights from data as needed.
- Consult on methodologies for large-scale modeling data (e.g., metabolomics, genetic).
- Interpret, summarize, and present data and statistical considerations to internal project teams, senior management, advisory board meetings, and global health authorities.
- Collaborate with Clinical Operations to select, oversee, contract, and manage Biostatistics vendors and contract research organizations.
- Teach Stats basics and create standard tools and practices to enable colleagues to visualize and gain insights from their data.
- Seek out exciting problems and use Statistics to solve them.
- Ph.D. in Statistics or Biostatistics with clinical development experience in multiple programs.
- Extensive knowledge of statistical methodology and its application to solve problems in a pharmaceutical/biotechnology setting.
- Experience supporting regulatory submissions, interacting with the FDA and global regulatory authorities.
- Strong analytical and problem-solving skills; expert collaboration and communication skills.
- Experience working with orphan indications preferred.
- Strong programming skills in R, Shiny, Markdown; working knowledge of Git and Unix/Linux environments.
- Familiarity with industry data standards, including CDISC, SDTM, and ADaM data models.
- Ability and interest to work cross-functionally.
- Highly independent, curious, and creative, can-do attitude, and embraces continuous improvement culture.