Associate Director, Statistics (Office based)

Role Summary

The Associate Director, Statistics - Oncology provides statistical leadership for clinical development and life-cycle management strategies for assigned projects, partnering with experts across disciplines to advance medicines to patients. This role supports clinical trials, patient safety, and global medical affairs, with a hybrid schedule at specified AbbVie locations.

Responsibilities

• Provide statistical support for one or more clinical development projects through own efforts or those of a team. Lead/develop statistical strategy for project development and regulatory submission.
• Provide statistical leadership for design, analysis and reporting for clinical or other scientific research programs. Independently develop protocols and statistical analysis plans.
• Represent function/department on project team(s) to provide statistical input to compound/drug development and drive alignment with functional management. Partner with other functions (Clinical, Regulatory, Patient Safety, and GMA, etc.) to create development strategies for assigned projects.
• Demonstrate extensive understanding of statistical concepts and methodology. Propose novel statistical methodological approaches to design of scientific studies. Provide sufficient detail to allow programming implementation. Ensure that all statistical analyses specified in scientific protocols and analysis plans are conducted appropriately.
• Train and mentor staff on statistical methodology and operations. May supervise contract statisticians or junior statisticians. Gain expertise in innovative statistical methods. Assist functional leaders in recruiting qualified personnel and arranging training opportunities for professional development of staff.
• Develop strategy for data presentation and inference. Ensure appropriate interpretation of statistical deliverables in collaboration with other functions. Collaborate in publication of scientific research. Ensure accuracy and internal consistency of reports and publications, including tables, listings, and figures.
• Act as the liaison for statistical issues on AbbVie collaborative studies with CROs, academic institutions, government agencies, steering and/or data monitoring committees, joint ventures or licensing collaborators. Build external scientific connections which foster professional development and promote the reputation of the Statistics Department.
• Ensure that all applicable regulatory requirements for work processes are met. Critically review regulatory submission documents. Participate in discussions with regulatory agencies as needed. Validate external statistical software to ensure compliance with SOPs and regulatory requirements.
• Significant Work Activities: Continuous sitting for prolonged periods (more than 2 consecutive hours in an 8 hr day) is required.

Qualifications

• MS (with 10+ years of experience) or PhD (with 6+ years of experience) in Statistics, Biostatistics, or a highly related field.
• High degree of technical competence and excellent communication skills, both oral and written.
• Able to identify data or analytical issues, and assist with providing solutions by either applying own skills and knowledge or seeking help from others.
• Able to manage project timeline and quality of deliverables.
• Able to build strong relationship with peers and cross-functional partners to achieve higher performance. Highly motivated to drive innovation by raising the bar and challenging the status quo.
• Strong leadership skills and experience in working/managing cross-cultural or overseas teams.
• Pharmaceutical or related industry knowledge required, including experience and understanding of drug development and life-cycle management in the regulated environment.
• (SSG) Experience leading safety-related projects (e.g., ISS, benefit-risk assessment, signal detection).
• (GMA Stat) Competence in experimental and real-world evidence study design, descriptive statistics, inferential statistics, statistical modeling, and statistical programming. Knowledge of methodologies for confounding control and bias minimization in observational studies preferred.