Senior Data Scientist, Biologics Discovery & ML

Johnson & Johnson; Titusville (NJ); On-site; USD 109,000 - 175,000; Full time

Benefits offered by this job

Annual bonus potential

Employee benefits

Job summary

Johnson & Johnson Innovative Medicine seeks a Senior Data Scientist to join the Data, Data Science & Artificial Intelligence (DDSAI) team. You will design featurization, curate datasets, and build ML models for biologics discovery, collaborating with ISD and Discovery teams to guide design-make-test-learn decisions.

The role focuses on creating model-ready data, ensuring reproducibility, and advancing molecular property modeling within a matrixed R&D environment.

Qualifications

• 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 with model development, evaluation, and dataset curation in complex scientific or biomedical data.
• Strong proficiency with Python and the modern ML stack (e.g., PyTorch, scikit-learn) and SQL.

Responsibilities

• Design robust featurization and dataset curation for biologics data.
• Build and evaluate applied ML models on assay, biophysical, and sequence data.
• Define evaluation frameworks to keep models trustworthy and reproducible.
• Collaborate with ISD, Discovery scientists, and MLOps for seamless data/model handoffs.

Skills

Applied ML

Python programming

Collaboration

Model evaluation

Feature engineering

Education

Master's or Ph.D. in Computer Science or related field

Tools

PyTorch

scikit-learn

SQL

Job description

Johnson & Johnson Innovative Medicine seeks a Senior Data Scientist to join the Data, Data Science & Artificial Intelligence (DDSAI) team. You will design featurization, curate datasets, and build ML models for biologics discovery, collaborating with ISD and Discovery teams to guide design-make-test-learn decisions.

The role focuses on creating model-ready data, ensuring reproducibility, and advancing molecular property modeling within a matrixed R&D environment.