Senior Research Statistician - Statistical Innovation (Hybrid)

AbbVie•North Chicago, IL

2d•$96,500 - $183,500•Hybrid

About The Position

The Senior Research Statistician in Statistical Innovation provides scientific and statistical leadership for initiatives focused on designing innovative clinical trials. The role evaluates, develops, and deploys advanced and novel statistical and analytical modeling and simulations to advance modeling and simulation in Clinical Development. It also supports the evaluation, introduction, development, and education of novel statistical and machine learning or deep learning approaches, and develops tools and techniques to operationalize innovative methodologies.

Requirements

PhD in Statistics, Biostatistics, or a highly related field.
Advanced knowledge of statistical modeling and methodology and a good understanding of the drug development process.
Demonstrated experience in advanced statistical computing and simulation.
Proven expertise in standard statistical software, such as SAS and R.
Ability to collaborate and consult with discipline experts and leaders within and across functions.
Excellent oral and written communication skills.
Excellent teamwork and leadership skills.
Passion for statistical innovation, an out-of-the-box mindset, and willingness to challenge the status quo.

Nice To Haves

Experience in pharmaceutical development and applied statistics or statistical consulting.
Expertise in specialized statistical software, such as FACTS or EAST.

Responsibilities

Support evaluation, introduction, development, and education of novel statistical and machine learning or deep learning approaches.
Investigate and evaluate complex innovative trial designs and analysis options using scientific computing, modeling, and simulations.
Acquire and apply knowledge of advanced and emerging statistical methods, including adaptive designs, Bayesian methods, synthetic control, and predictive modeling.
Create, invent, and implement new or improved approaches and ideas in modeling and simulations.
Apply statistical knowledge to real-world problems and evaluate alternative statistical and machine learning approaches.
Develop, implement, and roll out statistical and deep learning tools and techniques, using languages and platforms such as R, R-shiny, Python, and Julia.
Build and maintain an external reputation and influence the industry through presentations, industry meetings, or peer-reviewed publications.