Senior Scientist, Data Science

At Johnson & Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where treatments are smarter and less invasive, andsolutions 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

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Job Function:

Data Analytics & Computational Sciences

Job Sub Function:

Data Science

Job Category:

Scientific/Technology

All Job Posting Locations:

Madrid, Spain, Malvern, Pennsylvania, United States of America, Spring House, Pennsylvania, United States of America, Turnhout, Antwerp, Belgium

Job Description:

POSITION SUMMARY

The R&D Data, Data Science & AI (DDSAI) organization is seeking a Senior Scientist, Data Science to serve as a hands-on technical individual contributor who builds, tests, validates, and refines applied AI/ML and statistical models that support Therapeutics Development & Supply (TDS).

This individual contributor will partner with Process Development, CMC, Clinical Supply, Manufacturing, Quality, Regulatory, and Digital/Data Science teams to deliver practical, high-impact data science and machine learning models that support core TDS responsibilities across CMC development, pharmaceutical product development, dossier preparation, technical development, and clinical supply. The ideal candidate brings strong quantitative depth, a background in CMC development, fluency with data science tooling, and a proven record of delivering analytical solutions with real-world impact.

KEY RESPONSIBILITIES

Applied AI/ML Model Development

• Build, test, validate, and iterate on AI/ML models for TDS use cases under the guidance of senior data scientists and the AI Solutions Director.

• Translate complex scientific and business questions into tractable modeling problems, selecting fit-for-purpose methods based on the data, decision context, assumptions, and expected business impact.

• Conduct exploratory data analysis, feature engineering, model selection, hyperparameter tuning, and performance evaluation.

Technical Delivery & Software Development Discipline

• Develop clean, well-documented analytical code using Python and modern data science tooling, including cloud-based services

• Follow software engineering best practices: version control, testing, documentation, reproducibility, and code review.

• Use modern development environments, including AI-assisted coding tools where appropriate, to accelerate delivery while maintaining quality, transparency, and maintainability.

Cross-Functional Collaboration

• Work with TDS domain experts (scientists, engineers, quality professionals) to understand data, context, and requirements and design ML/AI solutions based on those requirements

• Support multiple concurrent projects, managing tasks and communicating progress to the team lead.

• Participate in team technical reviews, cross-functional capability groups, and capability-building activities.

QUALIFICATIONS

Required

• Advanced degree in Data Science, Statistics, Computer Science, Engineering, Chemistry, Physics, or a related quantitative discipline.
• 1–3 years of relevant experience in applied data science, machine learning, or statistical modeling (including graduate research).
• Proficiency in Python and common data science libraries (scikit-learn, pandas, numpy, PyTorch, TensorFlow).
• Strong analytical and problem-solving skills.
• Good communication skills and ability to work collaboratively in cross-functional teams.

Preferred

• Ph.D. in Mathematics, Statistics, Engineering, Chemical Engineering, Computer Science, Data Science, Operations Research, Chemistry, Physics, Pharmaceutical Sciences, or another related quantitative or scientific discipline.

• Experience with pharmaceutical, bioprocess, manufacturing, or healthcare data.

• Familiarity with experimental design, Bayesian methods, or scientific computing.

• Exposure to developing and/or deploying agent-based AI solutions

• Exposure to cloud-based da