Principal Data Scientist – R&D DSDH - Therapeutics Discovery (TD)

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Job Function

Data Analytics & Computational Sciences

Job Sub Function

Data Science

Job Category

Scientific/Technology

All Job Posting Locations:

Cambridge, Massachusetts, United States of America, La Jolla, California, United States of America, Spring House, Pennsylvania, United States of America

Job Description

Johnson & Johnson Innovative Medicine is recruiting for Principal Data Scientist – R&D DSDH - Therapeutics Discovery (TD)

The primary location for this position is open to Spring House, PA; Titusville, NJ; Spring House, PA; Cambridge, MA; San Diego, CA; Beerse, Belgium; Madrid, Spain; or Barcelona, Spain.

Candidate Interested in our EMEA based locations, please apply to R-069202

About The Role

Johnson & Johnson Innovative Medicine is seeking a highly skilled R&D Data Scientist to support our Therapeutics Discovery (TD) organization. This role sits within the R&D Data Science group and will focus on building and applying advanced Machine Learning (ML) and Data Engineering solutions that accelerate scientific innovation across the drug discovery lifecycle. The ideal candidate brings strong computational expertise and a solid scientific understanding of early R&D, including areas such as Target Identification & Assessment, Lead Identification & Optimization, Mechanistic / Mode of Action studies, and Lab Automation & high‑throughput experimentation.

The Data Scientist will collaborate closely with discovery scientists, automation engineers, computational biologists, and platform technology teams to transform complex, multimodal R&D data into actionable insights that drive therapeutic innovation.

Key Responsibilities

Machine Learning & Modeling

• 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.

Data Engineering & Pipeline Development

• 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
• Develop tooling that accelerates dataset preparation, feature engineering, and model lifecycle management across TD.

Scientific Partnership

• 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.

Innovation & Collaboration

• 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.
• Champion high standards for data quality, documentation, governance, and reproducibility.

Qualifications

Required

• 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.

Preferred

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