Principal Scientist, Data Science - Ddsai - Therapeutics Development Supply

Johnson & Johnson Johnson & Johnson · Pharma · Madrid, Spain +2

Seeking a Principal Data Scientist to design, build, validate, and deliver applied AI/ML and statistical modeling solutions for Drug Product Development & Supply (DPDS) within Johnson & Johnson Innovative Medicine. The role involves hands-on model development, translating scientific questions into modeling problems, and ensuring model robustness and actionability. It also requires applying software engineering best practices, collaborating with cross-functional teams, and providing technical guidance. Experience in pharmaceutical development or a related quantitative field is preferred.

What you'd actually do

  1. Serve as a senior hands-on individual contributor responsible for personally designing, coding, testing, validating, and refining applied AI/ML models for high-value TDS use cases.
  2. 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.
  3. Conduct rigorous exploratory analysis, feature engineering, diagnostics, error analysis, sensitivity analysis, and uncertainty assessment to ensure models are credible, robust, and actionable.
  4. Develop robust, reusable analytical workflows and modeling assets using Python and modern data science tooling, including cloud-based services.
  5. Apply software engineering best practices, including version control, modular code design, testing, documentation, reproducibility, and code review.

Skills

Required

  • Python programming
  • version control
  • testing
  • documentation
  • reproducibility
  • code review
  • applied problem-solving
  • stakeholder engagement
  • communication skills

Nice to have

  • Ph.D. in Mathematics, Statistics, Engineering, Chemical Engineering, Computer Science, Data Science, Operations Research, Chemistry
  • experience in pharmaceutical development
  • experience in manufacturing
  • experience in supply chain
  • experience in healthcare
  • experience in technical operations

What the JD emphasized

  • hands-on technical individual contributor
  • applied AI/ML and statistical modeling solutions
  • Drug Product Development & Supply (DPDS)
  • Therapeutics Development & Supply (TDS)
  • hands-on applied AI/ML model development
  • personally designing, coding, testing, validating, and refining applied AI/ML models
  • rigorous exploratory analysis
  • feature engineering
  • diagnostics
  • error analysis
  • sensitivity analysis
  • uncertainty assessment
  • robust, reusable analytical workflows
  • modern data science tooling
  • cloud-based services
  • software engineering best practices
  • version control
  • modular code design
  • testing
  • documentation
  • reproducibility
  • code review
  • modern development environments
  • AI-assisted coding tools
  • accelerate delivery
  • maintaining quality, transparency, and maintainability
  • Cross-Functional Collaboration
  • Technical Leadership
  • Business Impact
  • scientific, technical, and operations stakeholders
  • design data and AI/ML solutions
  • drive adoption
  • multiple concurrent projects
  • managing priorities
  • delivering maximum business value
  • TDS network
  • technical guidance
  • junior team members
  • consultants
  • post-doctoral researchers
  • collaborators
  • active hands-on contributor
  • measurable business impact
  • 3–5+ years of relevant experience
  • applied data science
  • machine learning
  • statistical modeling
  • AI/ML solution development
  • scientific computing
  • complex R&D
  • pharmaceutical development
  • manufacturing
  • supply chain
  • healthcare
  • technical operations environment
  • Strong Python programming skills
  • modern analytical development practices
  • Strong applied problem-solving skills
  • translate ambiguous scientific, technical, or business questions
  • rigorous quantitative solutions
  • Strong stakeholder engagement and communication skills
  • explain model assumptions, outputs, uncertainty, and implications
  • technical and non-technical audiences

Other signals

  • applied AI/ML model development
  • statistical modeling solutions
  • drug product development & supply