Statistics Leader / Associate Director of Statistics
GSK
4 months ago
Remote friendly (Collegeville, PA)
United States
Clinical Research and Development
Key Responsibilities
- Provide statistical leadership for oncology studies/indications, partnering with project and study teams to align strategy and execution
- Lead statistical strategy for study design, analysis, reporting, and interpretation, applying fit-for-purpose methods (e.g., simulation, interim strategies, Bayesian) to quantify uncertainty and inform decisions
- Author/review statistical sections of protocols, SAPs, clinical/regulatory documents, and presentations; communicate complex results clearly to non-statistical stakeholders
- Ensure high-quality, on-time deliverables and effective stakeholder management in a matrix environment
- Drive innovation in statistical methodology and data strategy, including appropriate use of external/real-world data to strengthen oncology evidence generation
- Build deep indication knowledge (e.g., regulatory expectations, competitor landscape) and influence asset strategy and regulatory submissions with rigorous statistical insight
- Represent Oncology Biostatistics externally as appropriate and may serve as project statistician for an oncology asset
Basic Qualifications
- PhD in Statistics (or related quantitative field) with at least 5 years post-degree experience, or MS/MSc in Statistics (or related quantitative field) with 8+ years of clinical development experience
- Experience supporting clinical development in pharmaceutical, biotech, or CRO settings
- Experience delivering fit-for-purpose statistical solutions and supporting study design and readouts
- Experience with R and/or Python
Preferred Qualifications
- PhD
- Oncology Clinical development experience (pharmaceutical, biotech, or CRO)
- Working knowledge of SAS
- Hands-on expertise in statistical modeling and innovative methods (e.g., Bayesian approaches)
- Knowledge of drug development process (late-phase, registration, oncology regulatory expectations)
- Familiarity with causal inference and/or AI/ML methods for clinical and/or real-world data
- Ability to lead or make major contributions to initiatives with communication and influence
- Strong communication and stakeholder-management skills in a matrix environment
- Highly organized, self-directed, able to manage multiple priorities
- Provide statistical leadership for oncology studies/indications, partnering with project and study teams to align strategy and execution
- Lead statistical strategy for study design, analysis, reporting, and interpretation, applying fit-for-purpose methods (e.g., simulation, interim strategies, Bayesian) to quantify uncertainty and inform decisions
- Author/review statistical sections of protocols, SAPs, clinical/regulatory documents, and presentations; communicate complex results clearly to non-statistical stakeholders
- Ensure high-quality, on-time deliverables and effective stakeholder management in a matrix environment
- Drive innovation in statistical methodology and data strategy, including appropriate use of external/real-world data to strengthen oncology evidence generation
- Build deep indication knowledge (e.g., regulatory expectations, competitor landscape) and influence asset strategy and regulatory submissions with rigorous statistical insight
- Represent Oncology Biostatistics externally as appropriate and may serve as project statistician for an oncology asset
Basic Qualifications
- PhD in Statistics (or related quantitative field) with at least 5 years post-degree experience, or MS/MSc in Statistics (or related quantitative field) with 8+ years of clinical development experience
- Experience supporting clinical development in pharmaceutical, biotech, or CRO settings
- Experience delivering fit-for-purpose statistical solutions and supporting study design and readouts
- Experience with R and/or Python
Preferred Qualifications
- PhD
- Oncology Clinical development experience (pharmaceutical, biotech, or CRO)
- Working knowledge of SAS
- Hands-on expertise in statistical modeling and innovative methods (e.g., Bayesian approaches)
- Knowledge of drug development process (late-phase, registration, oncology regulatory expectations)
- Familiarity with causal inference and/or AI/ML methods for clinical and/or real-world data
- Ability to lead or make major contributions to initiatives with communication and influence
- Strong communication and stakeholder-management skills in a matrix environment
- Highly organized, self-directed, able to manage multiple priorities