- Company Name
- Johnson & Johnson Innovative Medicine
- Job Title
- Senior Scientist, Discovery Data Science
- Job Description
-
Job title: Senior Scientist, Discovery Data Science
Role Summary:
Lead the design, development, and deployment of AI/ML solutions that accelerate drug discovery. Drive predictive modeling across chemical space and translate computational insights into actionable strategies for medicinal chemistry, assay development, and experimental biology. Partner with IT, MLOps, and cross‑functional scientific teams to operationalize models and shape discovery pipelines.
Expectations:
- Drive methodological innovation and proof of concept for AI/ML models that inform synthetic design and experimental prioritization.
- Translate complex data science results into clear narratives for scientists and stakeholders.
- Collaborate with computational chemists, medicinal chemists, biologists, toxicology, pharmacokinetics, and IT to integrate models into research workflows.
- Publish findings in peer‑reviewed journals and present at scientific conferences.
- Support data acquisition, generation, and alliance strategies for robust model training and validation.
Key Responsibilities:
- Design, train, and evaluate deep learning architectures (e.g., CNNs, GNNs) for predicting physicochemical, ADME, and biological activities beyond Lipinski constraints.
- Convert prototype models into production environments via MLOps pipelines; ensure scalability, reproducibility, and governance.
- Produce interpretable results and visualizations to guide medicinal chemists in molecule design and prioritization.
- Optimize laboratory protocols through predictive insights, leading to higher hit rates and reduced experimental costs.
- Maintain technical documentation, standard operating procedures, and best‑practice guidelines for data science workflows.
- Cross‑functional engagement to align model outputs with experimental objectives and timelines.
Required Skills:
- Expertise in machine learning, deep learning, and modern AI techniques for drug discovery.
- Proficiency in Python; hands‑on experience with PyTorch, TensorFlow, or Keras.
- Strong understanding of chemical informatics, cheminformatics libraries (RDKit, OpenEye, etc.), and chemical space representation.
- Experience with model deployment, MLOps tooling (Docker, Kubernetes, CI/CD), and data pipelines (SQL, NoSQL, ETL).
- Excellent scientific communication; ability to translate technical findings into clear, stakeholder‑friendly stories.
- Collaborative mindset with proven track record of cross‑disciplinary teamwork.
Required Education & Certifications:
- PhD in Data Science, Computational Science, Chemistry, Bioinformatics, or a closely related drug‑discovery field.