Principal Data Scientist – DDSAI GD

€61,800.00 - €106,260.00

In addition to base pay, we offer the following benefits*: an annual bonus with set target (% of pay) depending on pay grade / location, where the actual amount is based on the employees' and companies' performance of the previous calendar year, or sales commissions. Moreover, we offer vacation days, parental leave for a minimum of 12 weeks, bereavement leave, caregiver leave, volunteer leave, well-being reimbursement, programs for financial, physical and mental health. We also offer service anniversary and recognition awards, and subject to the terms of their respective plans, employees - and in some location's eligible dependents - can participate in several insurance plans. For more information, visit Employee benefits | Supporting well-being & career growth | Johnson & Johnson Careers.

*This is for informative purposes only. Amounts and actual benefits may vary by location and are subject to change.

Job Function: Data Analytics & Computational Sciences

Data Science

Scientific/Technology

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.

Key Responsibilities

• 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.