Associate Scientist, Data II

About AbbVie 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 including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com . Follow @abbvie on LinkedIn, Facebook , Instagram , X and YouTube.

Summary The doc management team is focused on transforming how pharmaceutical content is created, managed, reviewed, and delivered across the organization. This role will help accelerate the adoption of AI, automation, and modern data technologies to improve document authoring, quality control, knowledge management, and operational efficiency. The successful candidate will work at the intersection of data science, software engineering, and scientific business processes to develop intelligent solutions that streamline document-centric workflows. This includes leveraging large language models (LLMs), machine learning, knowledge management technologies, and system integrations to enable more efficient content creation, review, compliance, and reuse. The role will serve as a key partner in helping teams adapt emerging technologies and integrate them into everyday ways of working.

Partner with document management, regulatory, quality, and scientific teams to identify opportunities for AI-driven process improvement and workflow automation. Design, develop, and deploy solutions that support automated content authoring, content reuse, document summarization, metadata extraction, quality control, and compliance-related reviews. Build and maintain data pipelines, integrations, APIs, and knowledge management frameworks that connect document repositories, enterprise systems, and AI services. Develop and apply machine learning and generative AI solutions, including large language models (LLMs), retrieval-augmented generation (RAG), agentic workflows, and other modern AI technologies to improve document lifecycle management. Create scalable tools, applications, and user interfaces that enable business users to effectively leverage AI and automation capabilities. Evaluate emerging technologies and recommend practical solutions that can be adopted across the organization to increase productivity and operational effectiveness. Collaborate with business stakeholders to translate requirements into technical solutions while ensuring alignment with regulatory, quality, security, and governance standards. Establish best practices, training materials, and change management approaches that help teams successfully adopt new digital capabilities. Communicate technical concepts and project outcomes to both technical and non-technical audiences, driving understanding and adoption across diverse stakeholder groups.

Bachelor's degree in Computer Science, Data Science, Software Engineering, or a related quantitative field required. Relevant industry experience in pharmaceutical, biotechnology, content management, or AI-enabled digital transformation initiatives are preferred. Strong programming skills in Python and experience developing production-grade data, analytics, or AI applications. Experience designing data architectures, APIs, database solutions, and cloud-based applications using platforms such as AWS, Azure, or GCP. Hands-on experience with modern AI technologies, including large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), vector databases, AI agents, and document intelligence solutions. Experience working with enterprise content management systems, document repositories, knowledge management platforms, or workflow automation technologies is highly desirable. Strong understanding of