Principal Data Scientist – DDSAI GD

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com.

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.

Learn more at https://www.jnj.com/innovative-medicine

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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.