Senior Data Scientist, Biologics Discovery
Johnson & Johnson Innovative Medicine
Summary
Johnson & Johnson Innovative Medicine is seeking a Senior Data Scientist dedicated to our Biologics Discovery organization. This role sits within our Data, Data Science & Artificial Intelligence team (DDSAI) and partners closely with our In Silico Discovery (ISD) organization - the group that builds the molecular design and property-prediction models (for example, developability, affinity and binding, and other molecular-property and liability-risk models) that guide which biologic molecules to design, make, and advance. ISD owns core molecular model development; you will build the data-facing ML capabilities (featurization, model-ready datasets, evaluation frameworks, and applied models on assay and sequence data) that make ISD's models faster to build and better to trust.
Job description
About the opportunity
Johnson & Johnson Innovative Medicine is seeking a Senior Data Scientist dedicated to our Biologics Discovery organization. This role sits within our Data, Data Science & Artificial Intelligence team (DDSAI) and partners closely with our In Silico Discovery (ISD) organization - the group that builds the molecular design and property-prediction models (for example, developability, affinity and binding, and other molecular-property and liability-risk models) that guide which biologic molecules to design, make, and advance. ISD owns core molecular model development; you will build the data-facing ML capabilities (featurization, model-ready datasets, evaluation frameworks, and applied models on assay and sequence data) that make ISD's models faster to build and better to trust.
Key Responsibilities
- Develop featurization and model-ready datasets from antibody/protein sequence, construct, assay, and biophysical data.
- Work with data engineers to specify the features, labels, and levels of aggregation that models need, preserving raw representations where information matters.
- Curate, document, and version datasets so modeling is reproducible and traceable.
- Collaborate with ISD to hand off standardized, traceable training datasets and align on where Data Science enables versus where ISD owns modeling.
- Partner with Discovery scientists to frame ML problems around real decision points in the design-make-test-learn (DMTL) cycle.
- Work closely with ontology and MLOps colleagues so datasets carry consistent semantics and models move reliably from development into use.
- Champion reproducibility, documentation, and responsible AI.
Qualifications
Required: Master's or Ph.D. in Computer Science, Machine Learning, Computational Biology, Bioinformatics, Statistics, or a related field. At least 2 years of applied ML experience, including model development, evaluation, and dataset curation on complex scientific or biomedical data. Strong proficiency with Python and the modern ML stack (e.g., PyTorch, scikit-learn) and SQL. Experience turning complex, heterogeneous experimental data into robust features and training sets, with exposure to cloud training and data infrastructure. Sound understanding of evaluation, validation, and the risks of leakage and distribution shift. Ability to collaborate effectively with experimental scientists and modeling partners in a matrixed R&D environment.
Preferred: Experience with biologics, antibody/protein sequence models, or protein language models. Experience with active learning, Bayesian optimization, or sequence-based generative models for molecular design. Familiarity with biophysical/assay data and developability endpoints. Experience with MLOps, experiment tracking, and model monitoring. Familiarity with how ontologies or knowledge graphs support data reuse and AI-ready datasets.
Requirements
- Master's or Ph.D. in Computer Science, Machine Learning, Computational Biology, Bioinformatics, Statistics, or a related field
- At least 2 years of applied ML experience, including model development, evaluation, and dataset curation on complex scientific or biomedical data
- Strong proficiency with Python and the modern ML stack (e.g., PyTorch, scikit-learn) and SQL
- Experience turning complex, heterogeneous experimental data into robust features and training sets, with exposure to cloud training and data infrastructure
- Sound understanding of evaluation, validation, and the risks of leakage and distribution shift
- Ability to collaborate effectively with experimental scientists and modeling partners in a matrixed R&D environment
- Experience with biologics, antibody/protein sequence models, or protein language models (preferred)
- Experience with active learning, Bayesian optimization, or sequence-based generative models for molecular design (preferred)
- Familiarity with biophysical/assay data and developability endpoints (preferred)
- Experience with MLOps, experiment tracking, and model monitoring (preferred)
- Familiarity with how ontologies or knowledge graphs support data reuse and AI-ready datasets (preferred)
Benefits
- Medical insurance
- Dental insurance
- Vision insurance
- Life insurance
- Short-term disability
- Long-term disability
- Business accident insurance
- Group legal insurance
- Pension plan
- 401(k) savings plan
- Vacation time
- Sick time
- Holiday pay
Company overview
Johnson & Johnson Innovative Medicine is the pharmaceutical division of Johnson & Johnson, leading the way in medical innovation with a relentless focus on bringing transformational treatments to patients worldwide. The company applies rigorous science and compassion to address the most complex diseases of our time, developing groundbreaking medicines across key therapeutic areas including oncology, immunology, neuroscience, and cardiopulmonary medicine. With deep expertise and global reach, Johnson & Johnson Innovative Medicine reimagines how devastating diseases are treated and cured, delivering hope and better care for millions of patients and their families. The organization pioneers the path from laboratory research to clinical application, championing patients every step of the way while maintaining an unwavering commitment to scientific excellence and patient-centered innovation.