Senior Data Scientist, Biologics Discovery

About the role

This Senior Data Scientist role sits within the Data, Data Science & Artificial Intelligence team (DDSAI) and partners closely with our In Silico Discovery (ISD) organization. The position is dedicated to building the data-facing ML capabilities that enable ISD’s molecular design and property-prediction models (e.g., developability, affinity, binding, and other molecular-property and liability-risk models) to be faster to build and better to trust. The role will be based in Spring House, PA (strongly preferred), Titusville, NJ, Raritan, NJ, or Madrid, Spain, with no remote option.

Why this role matters

Biologics Discovery is generating rich, fast-growing data across assays, sequences, and modalities. This role ensures that data is structured for training and that applied ML on discovery data helps scientists prioritize molecules, flag risks, and generate hypotheses earlier—strengthening the interface to ISD’s models rather than duplicating them.

Responsibilities

• Design robust featurization and dataset curation pipelines for antibody/protein sequence, construct, assay, biophysical, and related data.
• Collaborate with data engineers to specify features, labels, and aggregation levels, preserving raw representations where information matters.
• Curate, document, and version datasets to ensure reproducibility and traceability.
• Develop and evaluate applied ML models on biologics assay, biophysical, and sequence/construct data.
• Define evaluation frameworks that keep models trustworthy and aligned with scientific decision-making.
• Partner with ISD to hand off standardized, traceable training datasets and clarify where Data Science enables versus where ISD owns modeling.
• Collaborate with Discovery scientists to frame ML problems around real decision points in the design–make–test–learn (DMTL) cycle.
• Work with ontology and MLOps colleagues to ensure datasets carry consistent semantics and models move reliably from development into use.
• Champion reproducibility, documentation, and responsible AI practices.

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, 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

• 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.