Information Scientist - Content Analytics

Are you ready to shape the future of biomedical information? Join a dynamic and innovative team at the forefront of scientific discovery, where your passion for information science and curiosity about artificial intelligence will drive meaningful impact. As an Information Scientist specializing in Content Analytics, you'll harness advanced AI tools and data analytics to unlock insights from the world's biomedical literature. Your expertise will help design smarter workflows, champion ethical and responsible AI practices, and enable cross-functional collaboration across pharmaceutical R&D. If you thrive in a fast-evolving environment, love exploring new technologies, and want to empower others through training and knowledge sharing, this is your chance to make a difference.

• Content Analytics & Knowledge Management:
  • Design and implement workflows for literature mining, summarization, and trend analysis using AI/NLP tools.
  • Develop and maintain external information endpoints, ensuring FAIR principles (Findable, Accessible, Interoperable, Reusable) and interoperability.
• AI & Advanced Analytics:
  • Apply machine learning and large language models (LLMs) to enhance information retrieval and automate content classification.
  • Research and propose innovative AI solutions for scientific content analysis.
• Information Science:
  • Support pharmaceutical R&D processes.
  • Apply knowledge of data lifecycle, metadata, ontologies, and scientific knowledge systems.
  • Ensure data is structured for scientific use, leveraging FAIR data principles and semantic technologies.
• Collaboration & Stakeholder Engagement:
  • Partner with cross-functional teams as required.
  • Act as a subject matter expert for AI-driven content analytics initiatives.
• Governance & Compliance:
  • Ensure adherence to copyright, licensing, and data governance standards for external and internal content.
  • Implement robust data integrity measures and maintain audit trails for AI-driven processes.
• Training & Enablement:
  • Provide training on AI/NLP tools and best practices for content analytics.
  • Develop guidelines and competency frameworks for upskilling in digital and data science capabilities through the application of content analytics.

• Technical Skills:
  • Experience or interest in NLP and LLM applications (transformers, Retrieval-Augmented Generation, prompt engineering)
  • Familiarity with data visualization and scientific communication tools
  • Knowledge graph and ontology management
  • Python and AI frameworks for text analytics
• Soft Skills:
  • Cross-functional collaboration
  • Project management and stakeholder communication
  • Adaptability to emerging technologies