Responsibilities

Contribute to the development of a portfolio of RWE projects based on RWD that will provide key insights to our pipeline assets

Leverage emerging scientific and technological developments to generate new research ideas, solutions and initiatives using real-world data

End-to-end experience in RWE studies including conceptualizing the research questions, data feasibility, study design, analysis, programming, and interpretation

Analyze and interpret data to support urgent requests from internal and external stakeholders

Ensure quality of design, execution, and publication of real-world evidence studies, and quality of models & tools

Create study protocols, statistical analysis plans, and statistical programming deliverables

Clearly articulate highly technical methods and results to diverse audiences and partners to drive decision-making

Ensure RWE generation aligned with regulatory requirements and scientific standards

Requirements

A Ph.D. degree, or master’s degree in a quantitative field (e.g., biostatistics, AI Data Science, Bioinformatics, similar)

Relevant experience (2+ years for Ph.D., 4+ years for a master’s) within biopharma companies, RWE consulting firms, or other relevant healthcare industries

Experience designing and executing studies using real-world healthcare data, such as claims, electronic health records (EHRs),



or registries

Extensive hands‑on experience with data engineering and data analysis

Proven track record of consistently delivering on high impact data science projects

Expert proficiency in either R or Python, SPARQL, Cypher

Excellent interpersonal, communication, and presentation skills

Experience delivering on Data Science projects using predictive technologies as machine learning/deep learning, or forecasting

Familiarity with and exposure to drug discovery and clinical development processes with one or more of the following therapeutic areas: oncology, immunology, neuroscience, or specialty ophthalmology

Hands‑on experience with graph neural networks and related graph ML methods for learning across large, heterogeneous, multi-domain graphs.

Hand‑on bioinformatics experience running and interpreting results in a biological/disease context, including pathway or functional enrichment analysis.

Core Competencies Demonstrates expertise in Real-World Evidence (RWE) study design, data analysis, and programming, with a strong focus on delivering impactful data science projects in the biopharma sector. Proficient in communicating complex methodologies and results to diverse stakeholders while ensuring compliance with regulatory standards.

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📌 Principal Data Scientist, Real World Evidence (Madrid)
🏢 Jobtailor
📍 Madrid