Data Scientist II for Published Clinical Evidence & Competitive Intelligence Insights

Role Summary

Data Scientist II for Published Clinical Evidence & Competitive Intelligence Insights will join the Solutions for Published Insights and Client Enablement (SPICE) team, supporting AbbVie’s R&D, CBSO and other business functions enterprise wide. The role involves developing novel AI-based capabilities for extracting, harmonizing, and monitoring published clinical trial data, real-world evidence, and other competitive insights, collaborating with internal expert teams to define requirements and integrate manually curated data and insights to support pipeline strategy, corporate strategy, and commercial teams.

Responsibilities

• Leverage scientific domain and technical knowledge to support clients across AbbVie with the most relevant information and published insights for decision-making. Key focus: Competitive Intelligence, Corporate and Pipeline Strategy groups and data.
• Build and design new methods and automated workflows to systematically identify, extract, normalize and database key insights and evidence from publications as well as other published sources using GenAI and other state-of-the-art technology and data science solutions
• Work with key stakeholders and expert teams to identify novel sources for manually curated competitive intelligence and clinical trial data and integrate in existing workflows to democratize access across AbbVie
• Support efforts for streamlining AbbVie’s systematic literature review process and knowledge extraction from (full-text) publication resources
• Provide unique scientific insights and expertise by designing and developing solutions for finding, extracting, curating, and visualizing knowledge from published and internal data
• Monitor and be attuned to new technology trends relevant for knowledge analysis and insights discovery
• Achieve great results, while overwhelmingly demonstrating key AbbVie values and behaviors.

Qualifications

• Required: Bachelor’s degree (with 5 years of experience), Master’s (4 years), or Ph.D. (0-2 years) in life sciences, medicine, pharmacy, bioinformatics, biomedical/clinical data science, or related field
• Required: Solid scientific domain knowledge in late-stage pharmaceutical development—Medical Affairs, Clinical Development, Pharmacovigilance, HEOR, Epidemiology—proven by education or work experience
• Required: Solid programming and informatics/data science/computer science background. Proficiency in Python.
• Required: Experience analyzing publications, especially scientific literature
• Required: Proven ability to translate complex scientific or clinical questions into actionable solutions and insights
• Required: Initial experience with data consolidation, standardization, optimization, or exploratory data analysis
• Required: Experience with relational databases and SQL (e.g., Oracle, Apache Hadoop)
• Required: Strong analytical skills to process, analyze, visualize, and present results
• Required: Systematic problem-solving mindset, reliability, superior attention to detail
• Required: Good communication skills for interdisciplinary teams and various audiences
• Required: Good organizational skills: balance taking direction (from sponsors, partners, clients) with initiative for multiple projects
• Required: Comfortable working in a dynamic, collaborative, fast-paced environment with constantly shifting priorities
• Required: Innate scientific curiosity, technical creativity, and innovative thinking. Motivated to break new ground in the field of information analysis and insights generation.
• Required: Fluency in English
• Preferred: Pharmaceutical industry experience
• Preferred: Experience with front-end technologies (HTML, JavaScript, CSS, React)
• Preferred: Basic working experience with Linux/Unix-based OS and Docker
• Preferred: Experience with GenAI models for knowledge extraction, their pitfalls, prompting and embedding them within Python workflows
• Preferred: Basic understanding of agents/agentic workflows (MCP, A2A)
• Preferred: Experience with drug pipeline or trial competitive intelligence databases (e.g., Cortellis, Trialtrove, Pharmaprojects)
• Preferred: Experience with data pipeline frameworks, text mining, NLP, semantic enrichment, ontologies, or data mining
• Preferred: Knowledge of technologies for retrieval, analysis, and visualization (e.g., REST APIs, XML, JSON, Python libraries like Pydantic)