Principal Data Scientist

Company Description

AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas – immunology, oncology, neuroscience, and eye care – and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on X, Facebook, Instagram, YouTube, LinkedIn and Tik Tok.

Job Description

The Oncology Early Development (OED) Principal Data Scientist is a key Development Operations Leadership role in the management of clinical data and analytics, ensuring strategic insights through analysis of data and information across the AbbVie OED portfolio. This is a role critical to ensuring the strong analytical capability of OED through the leveraging of emerging technologies including language models/AI, and data from various internal and external sources.  In this role, the Principal Data Scientist’s primary focus will be building analytical capabilities of the department while collaborating with leaders and key stakeholders to support deep, analytical understanding of OED’s Clinical Operations portfolio.

• Partner with cross-functional teams to identify new opportunities requiring the use of modern analytical and modeling techniques; Lead the design, analysis, and interpretation of projects from data requirement gathering to data processing, modeling, and recommendations
• Build production ready advanced analytics solutions to drive effective business and scientific decisions
• Drive the definition, design, implementation and validation of cutting-edge algorithms to analyze disparate data sources to achieve targeted outcomes by leveraging machine-learning and Artificial Intelligence approaches, predictive and statistical modeling concepts, clustering and classification techniques
• Design experiments to answer key analytical questions that to drive speed and efficiency within the oncology early clinical development pipeline
• Own the full lifecycle of model development from ideation and data exploration, algorithm design and testing, algorithm development and deployment, to algorithm monit