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# Principal Data Scientist - DDSAI GD

- **Company:** J&J Family of Companies
- **Location:** Madrid, Spain
- **Salary:** €61,800.0 - €106,260.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Health Informatics, Code Reuse, Communications Protocols, Computer Literacy, Data Visualization, Digital Assets, Information Extraction, Python (Programming Language), SQL Databases, Large Language Models, Generative AI, Information Technology, Machine Learning Operations, Meditech
- **Published:** October 30, 2026
- **Apply:** https://dejobs.org/x/x/1C4573EC3D9446FD8B038CE921EDA7CD/job/

## About the Role

* Master's degree in computer science, engineering, biostatistics, health economics, biomedical informatics, applied mathematics, or another quantitative discipline; PhD preferred.
 * At least 5 years of industry experience leading end-to-end delivery of data-science solutions using ML, predictive modeling, optimization, NLP, GenAI, or RWD analytics.
 * Demonstrated experience defining, validating, deploying, and driving adoption of analytical solutions that inform clinical-development, clinical-operations, or real-world evidence decisions.
 * Hands-on experience with structured and unstructured clinical, operational, or RWD data, including multimodal modeling and time-series or simulation methods.
 * Proficiency in Python and SQL, modern ML, optimization, and LLM-development libraries, and reproducible MLOps practices.
 * Strong communication, stakeholder-management, and technical-leadership skills., * Experience in healthcare, pharmaceutical development, MedTech, clinical development, or clinical operations.
 * Experience applying RWD methods to clinical-trial design, including target-trial emulation, propensity methods, survival analysis, or time-to-event analysis.
 * Experience delivering analytical products or applications used by clinical or business teams.

Required Skills:

Preferred Skills:

Advanced Analytics, Coaching, Critical Thinking, Data Analysis, Data Privacy Standards, Data Quality, Data Reporting, Data Savvy, Data Science, Data Visualization, Digital Fluency, Econometric Models, Organizing, Process Improvements, Strategic Thinking, Technical Credibility, Workflow Analysis

## Description

J&J Innovative Medicine - Data, Data Science, and AI - Global Development (DDSAI GD) is seeking a Principal Data Scientist to lead end-to-end delivery of integrated analytical solutions for defined clinical-development and trial-operations business problems. This role owns solution definition, delivery, adoption, and use-case outcomes across connected stakeholder groups using ML, GenAI, optimization, and RWD., * Own the end-to-end definition, development, validation, deployment, and adoption of integrated data-science solutions for defined business problems.

 * Lead predictive modeling, simulations, optimization, and GenAI applications for study feasibility, enrollment forecasting, country and site selection, protocol design, and operational planning.
 * Apply clinical, operational, cost, and RWD-including EHR, claims, registries, and clinical-trial data-to generate evidence on patient populations, treatment pathways, feasibility, and operational risks.
 * Lead RWD analytics that inform protocol feasibility, eligibility criteria, recruitment and retention, study design, comparative evidence, and external-control use cases.
 * Develop LLM-enabled workflows for information extraction, protocol comparison, data harmonization, clinical-concept interpretation, and targeted workflow automation.
 * Partner with clinical, operational, technology, and data stakeholders to frame questions, manage trade-offs, and translate outputs into adopted recommendations and measurable use-case outcomes.
 * Apply model-validation, MLOps, documentation, privacy, data-quality, and responsible-AI standards; contribute reusable code, data assets, and technical patterns.
 * Communicate methods, model outputs, assumptions, limitations, and decisions clearly to technical and nontechnical audiences.
 * Mentor junior colleagues and strengthen delivery practices across project teams.