Manager, Statistics

AbbVie•North Chicago, IL

9d•$109,500 - $208,500•Hybrid

About The Position

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.

Requirements

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.
Advanced programming proficiency in R, including development of reusable, tested, production-grade functions or modules and interactive Shiny applications, with demonstrated experience delivering validated analytical software in a regulated (GxP) environment.
Hands-on experience applying machine learning or AI methods, including large language models or agentic workflows, to scientific, analytical, or workflow-automation use cases with appropriate human oversight preferred.

Nice To Haves

Pharmaceutical or related industry knowledge desired, including experience and understanding of drug development and life-cycle management in the regulated environment.

Responsibilities

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 datasets.
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.
Lead the development, validation, documentation, and lifecycle maintenance of reusable statistical analysis codes, analysis templates, benchmark test sets, and quality-control assets, applying reproducible programming practices with attention to version control, traceability, cross-study reusability, and applicable GxP expectations.
Design and deliver AI-enabled analytical workflows, including workflow architecture, tool-use or retrieval components, evaluation of model outputs, human-in-the-loop review, testing, and safeguards that preserve scientific rigor, interpretability, and reproducibility in a regulated environment.
Act as a technical lead partnering with cross-functional stakeholders and external collaborators to translate end-user requirements into maintainable, production-grade solutions; drive adoption through interactive interfaces (e.g., R/Shiny), technical documentation, training materials, and knowledge transfer.