Principal Data Scientist – R&D DSDH, Therapeutics Discovery
Johnson & Johnson
full-time
Posted on:
Location Type: Hybrid
Location: Madrid • Spain
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Job Level
About the role
- Develop ML/AI models that support discovery workflows, including target prioritization, multi‑omics integration, and mechanistic inference.
- Apply modern ML approaches (e.g., deep learning, graph learning, foundation models, generative models) to chemical, biological, imaging, and assay datasets.
- Build and optimize models for real‑world R&D use cases, ensuring scalability, interpretability, and scientific rigor.
- Design, build, and maintain robust data pipelines that curate, standardize, and integrate diverse R&D datasets (chemical, biological, multi‑omics, imaging, biophysical, automation logs, etc.)
- Partner with platform teams to implement best‑practice MLOps/DevOps workflows and deploy ML models into production R&D environments.
- Work hand‑in‑hand with TD scientists to understand key biological and chemical questions and shape computational strategy accordingly.
- Translate sparse, heterogeneous experimental datasets into insights that guide decision‑making in hit discovery, mechanism studies, perturbation experiments, and compound optimization.
- Participate in design, interpretation, and iterative refinement of discovery experiments.
- Partner with cross-functional teams in R&D Data Science, IT, platform engineering, and therapeutic area groups to drive AI/ML adoption.
- Contribute to evaluating new analytical methods, automation technologies, and data platforms supporting next‑generation discovery science.
Requirements
- Master’s or Ph.D. in Computational Biology, Bioinformatics, Data Science, Chemistry, Chemical Biology, Biomedical Engineering, Computer Science, or related field
- Experience applying ML/AI in scientific domains (drug discovery, biology, chemistry, systems biology, imaging, or related areas)
- Strong programming skills in Python (preferred) and experience with scientific/ML libraries (PyTorch, TensorFlow, scikit‑learn, RDKit, etc.)
- Practical experience with data engineering, including data modeling, workflow orchestration, ETL/ELT pipelines, and cloud computing environments (AWS, GCP, or Azure)
- Ability to work directly with experimental scientists to solve real R&D challenges.
Benefits
- Inclusive work environment
- Professional development opportunities
- Commitment to diversity and dignity
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningartificial intelligencedeep learninggraph learningfoundation modelsgenerative modelsdata engineeringETLdata modelingworkflow orchestration
Soft Skills
collaborationproblem-solvingcommunicationinterdisciplinary teamworkanalytical thinking
Certifications
Master’s degreePh.D.