The Manager, Statistics provides statistical expertise to support the research and development organization. Specific areas of work may include clinical trials, patient safety, and global medical affairs. The Manager works independently in partnership with experts in multiple disciplines to advance medicines to our patients.
Key Responsibilities Include:
Key Responsibilities Include:
- Provide expertise to design, analysis and reporting of clinical trials or other scientific research studies. Independently develop protocols and/or statistical analysis plans (or product safety analysis plans/integrated summary of safety analysis plans/analysis plans for GMA evidence generation) with details for programming implementation. Implement sound statistical methodology in scientific investigations.
- Identify scientifically appropriate data collection instruments. Identify and report data issues or violations of study assumptions. Provide programming specifications for derived variables and analysis data sets. Partner with Data Science in preparing for database lock.
- Independently perform statistical analyses as per the analysis plan. Collaborate with Statistical Programming to ensure the delivery of high-quality outputs according to agreed-upon timelines. Identify and anticipate issues arising in the study design, conduct and propose scientifically sound approaches. Evaluate appropriateness of available software for planned analyses and assess needs for potential development of novel statistical methodology.
- Develop strategy for data presentation and inference. Collaborate in publication of scientific research. Ensure accuracy and internal consistency of reports and publications, including tables, listings, and figures. Ensure that study results and conclusions are scientifically sound, clearly presented, and consistent with statistical analyses provided.
- Work collaboratively with multifunction teams. Clearly explain statistical concepts to non-statisticians. Provide responses to questions, and pursue analyses suggested by data. Support communications between assigned product team(s) and functional management. Build/drive cross-functional relationships and collaboration.
- (SSG) Collaborate with cross functional team for benefit-risk planning and assessment. Contribute to cross functional development of output specifications to address both pre-planned safety analyses and ad hoc requests.
- (SSG) Collaborate/lead within the Safety Statistics Group to implement strategic initiatives that address processes related to interpreting, monitoring, assessing, and reporting safety data to characterize the safety profile of AbbVie products, improve efficiencies, and provide consistency across therapeutic areas.
- (GMA Stat) In collaboration with GMA, Clinical Statistics, Data Sciences, Statistical Programming and other stakeholders to evaluate existing databases, both clinical studies and real-world databases, conduct feasibility assessment to identify fit-for-purpose data sources to address research questions, and develop detailed and actionable analysis plans for evidence generation to deliver high quality, patient-centric evidence and insights to drive decisions.
- MS (with 6+ years of experience) or PhD (with 2+ years of experience) in Statistics, Biostatistics, or a highly related field.
- High degree of technical competence and effective communication skills, both oral and written
- Able to perform statistical computations and simulations
- 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 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
- Pharmaceutical or related industry knowledge desired, including experience and understanding of drug development and life-cycle management in the regulated environment.
- (GMA Stat) Competence in experimental and RWE study design, descriptive statistics, inferential statistics, statistical modeling, and statistical programming. Knowledge of methodologies for confounding control and bias minimization in observational studies preferred.