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

Come to work each day with an inclusive and collaborative business technology team. As a Data Scientist in AbbVie Business Technology Solutions (BTS), you will have opportunities to contribute to the digital transformation of a leading biopharma company, helping to create solutions that impact patients and their communities for the better.

Responsibilities:

• A successful candidate will bring advanced analytics knowledge of best-in-class data science methodologies, deep understanding of AI/ML space, proven history of solving business problems using data science and ability to work with cross-functional teams to deliver and execute complex analytics problems.
• Experience with Natural Language Processing (NLP) techniques, LLM prompt engineering, LLM-based solution architecture such as retrieval-augmented generation and AI Agent development.
• Develop processes and tools to monitor and analyze model performance and data accuracy and lead the advanced analytics framework through AI/ML.
• Experience in working with complex data sets (unstructured and structured data) to solve business use cases by establishing robust measurement frameworks and deliver business value with data-driven insights to senior leaders.
• Experience in data discovery and analysis to discover trends and patterns, meaningful insights and apply strong knowledge of data science for making informed decisions.
• Design/Develop data visualizations to effectively communicate insights to both technical and non-technical audiences.
• Establish and follow data science’s best practices including peer review, code review, documentation, coding standards, data quality and ensure reproducibility and compliance