Senior Data Scientist, Biologics Discovery — Shape Models

Relha LLC; Northern (KY); Hybrid; USD 109,000 - 175,000; Full time

Job summary

Johnson & Johnson Innovative Medicine is seeking a Senior Data Scientist dedicated to our Biologics Discovery organization. This role sits within the Data, Data Science & Artificial Intelligence team (DDSAI) and partners closely with our In Silico Discovery (ISD) organization—building data-facing ML capabilities to accelerate biologics design and evaluation.

The position focuses on creating robust feature pipelines from sequences, assays, and biophysical data, while ensuring reproducibility and

Qualifications

• Master's or Ph.D. in Computer Science, ML, Computational Biology, Bioinformatics, Statistics.
• At least 2 years of applied ML experience.
• Strong proficiency with Python and the modern ML stack (PyTorch, scikit-learn) and SQL.
• Experience turning complex, heterogeneous experimental data into robust features and training sets.
• Ability to collaborate with experimental scientists and modeling partners in a matrixed R&D environment.

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.
• Champion reproducibility, documentation, and responsible AI.

Skills

Python

PyTorch

scikit-learn

SQL

Education

Master's or Ph.D. in Computer Science, ML, Computational Biology, Bioinformatics, Statistics

Tools

Cloud training

Job description

Johnson & Johnson Innovative Medicine is seeking a Senior Data Scientist dedicated to our Biologics Discovery organization. This role sits within the Data, Data Science & Artificial Intelligence team (DDSAI) and partners closely with our In Silico Discovery (ISD) organization—building data-facing ML capabilities to accelerate biologics design and evaluation.

The position focuses on creating robust feature pipelines from sequences, assays, and biophysical data, while ensuring reproducibility and