Leads hands-on data science at Novo Nordisk, turning observational patient-level data (EHR, claims, registries) into decision-ready real-world evidence for early R&D and portfolio decisions. Core work applies causal inference, statistical modelling, ML/deep learning and agentic AI in Python/R, using cloud HPC and reproducible research practices.
As a Data Science Specialist in computational real-world evidence (cRWE) at Novo Nordisk, you lead high-priority data science efforts to convert observational patient data into decision-ready evidence for early R&D. You balance hands-on analytics with strategic guidance across disciplines, translating questions into robust analyses that inform portfolio decisions. You apply causal inference, ML, and AI to real-world data to address disease understanding, target discovery, and development milestones. This role offers cross-functional collaboration and a chance to shape translational science with agenticAI capabilities.
Pay / Benefits
incentives and competitive salary package
benefits designed around career and life stage
global healthcare company culture
opportunities for learning and development
inclusive recruitment process
cross-functional collaboration and international teamwork
Responsibilities
Lead high-priority data science projects supporting preclinical and early development decision-making
Develop and implement analytical and agenticAI approaches across causal inference, statistical modelling, ML, deep learning, and multimodal data analysis
Translate biological, clinical, and portfolio questions into executable analysis plans and actionable evidence
Drive scientific quality, reproducibility, and rigorous interpretation of uncertainty, bias, and causality across projects
Collaborate with experts across research, biology, epidemiology, translational medicine, clinical development, and data science to produce evidence impacting portfolio decisions
Provide hands-on analytical leadership and strategic guidance to ensure questions are translated into robust analytical approaches
Key requirements
PhD or equivalent in a quantitative field
Strong expertise in statistical modelling, machine learning, AI or related discipline with ability to select and evaluate methodologies
Significant experience with longitudinal patient-level datasets (electronic health records, claims, registries, cohorts)
Significant experience with causal inference, longitudinal modelling, clustering, survival analysis, and modern AI approaches in biomedical research
Strong programming skills in Python and/or R; experience in cloud-based HPC and reproducible research practices
Experience supporting drug discovery, translational research, target validation, or early development (advantageous)