Senior Data Scientist, Biologics Discovery
Johnson & Johnson
September 01, 2026
On-site
Raritan, NJ
Clinical Research and Development
About The Opportunity
Senior Data Scientist (Biologics Discovery) within Data, Data Science & AI (DDSAI), partnering with In Silico Discovery (ISD). Build data-facing ML capabilities (featurization, model-ready datasets, evaluation frameworks, applied models on assay and sequence data) to make ISD’s molecular property models faster and more trustworthy.
Position Summary
Design featurization and dataset curation, build/evaluate applied models on biologics assay, biophysical, and sequence/construct data, and define evaluation frameworks that keep models trustworthy. Shape how AI learns from every biologics experiment.
Key Responsibilities
- Develop featurization and model-ready datasets from antibody/protein sequence, construct, assay, and biophysical data.
- Collaborate with data engineers to define features/labels/aggregation levels; preserve raw representations.
- Curate, document, and version datasets for reproducibility and traceability.
- Partner with ISD to hand off standardized, traceable training datasets; align ownership boundaries.
- Frame ML problems around real decision points in the design-make-test-learn (DMTL) cycle.
- Work with ontology and MLOps teams to ensure consistent semantics and reliable model movement into use.
- Champion reproducibility, documentation, and responsible AI.
Qualifications
Required: Master’s/PhD (CS/ML/Computational Biology/Bioinformatics/Statistics or related); 2+ years applied ML (model development/evaluation/dataset curation) on scientific/biomedical data; Python (PyTorch/scikit-learn) and SQL; experience with heterogeneous experimental data → features/training sets; understanding of evaluation/validation, leakage, and distribution shift; collaboration in matrixed R&D.
Preferred: Biologics/antibody-protein/protein language models; active learning/Bayesian optimization/generative sequence models; familiarity with biophysical/assay data and developability endpoints; MLOps/experiment tracking/model monitoring; ontologies/knowledge graphs for AI-ready datasets.
Compensation & Benefits (as stated)
Anticipated base pay: $109,000–$174,800; annual performance bonus eligibility; medical/dental/vision/life insurance and disability; 401(k)/pension; time off: vacation up to 120 hours/year, sick up to 40 hours/year, floating/holiday pay up to 13 days/year (plus Work/Personal/Family time up to 40 hours/year).
Senior Data Scientist (Biologics Discovery) within Data, Data Science & AI (DDSAI), partnering with In Silico Discovery (ISD). Build data-facing ML capabilities (featurization, model-ready datasets, evaluation frameworks, applied models on assay and sequence data) to make ISD’s molecular property models faster and more trustworthy.
Position Summary
Design featurization and dataset curation, build/evaluate applied models on biologics assay, biophysical, and sequence/construct data, and define evaluation frameworks that keep models trustworthy. Shape how AI learns from every biologics experiment.
Key Responsibilities
- Develop featurization and model-ready datasets from antibody/protein sequence, construct, assay, and biophysical data.
- Collaborate with data engineers to define features/labels/aggregation levels; preserve raw representations.
- Curate, document, and version datasets for reproducibility and traceability.
- Partner with ISD to hand off standardized, traceable training datasets; align ownership boundaries.
- Frame ML problems around real decision points in the design-make-test-learn (DMTL) cycle.
- Work with ontology and MLOps teams to ensure consistent semantics and reliable model movement into use.
- Champion reproducibility, documentation, and responsible AI.
Qualifications
Required: Master’s/PhD (CS/ML/Computational Biology/Bioinformatics/Statistics or related); 2+ years applied ML (model development/evaluation/dataset curation) on scientific/biomedical data; Python (PyTorch/scikit-learn) and SQL; experience with heterogeneous experimental data → features/training sets; understanding of evaluation/validation, leakage, and distribution shift; collaboration in matrixed R&D.
Preferred: Biologics/antibody-protein/protein language models; active learning/Bayesian optimization/generative sequence models; familiarity with biophysical/assay data and developability endpoints; MLOps/experiment tracking/model monitoring; ontologies/knowledge graphs for AI-ready datasets.
Compensation & Benefits (as stated)
Anticipated base pay: $109,000–$174,800; annual performance bonus eligibility; medical/dental/vision/life insurance and disability; 401(k)/pension; time off: vacation up to 120 hours/year, sick up to 40 hours/year, floating/holiday pay up to 13 days/year (plus Work/Personal/Family time up to 40 hours/year).