Senior Data Scientist, Biologics Discovery

What you'll do

• Develop featurization and model-ready datasets from antibody, protein sequence, assay, and biophysical data.
• Collaborate with data engineers to define features, labels, and aggregation levels for machine learning models.
• Curate, document, and version datasets to ensure modeling is reproducible and traceable.
• Build and evaluate applied machine learning models on biologics discovery data.
• Define evaluation frameworks to ensure model reliability and trustworthiness.
• Partner with Discovery scientists to frame machine learning problems around key decision points in the research cycle.
• Work with MLOps colleagues to ensure datasets maintain consistent semantics and move reliably into production.

What we're looking for

• 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.
• Proficiency with Python, SQL, and the modern ML stack such as PyTorch and scikit-learn.
• Experience transforming heterogeneous experimental data into robust features and training sets using cloud infrastructure.
• Understanding of evaluation, validation, and risks like leakage and distribution shift.
• Ability to collaborate with experimental scientists and modeling partners in a matrixed R&D environment.
• Preferred 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.