Senior Data Scientist

Key Responsibilities

Develop and apply predictive machine learning models for biologics properties, including developability and clinical immunogenicity-related endpoints

Train, fine-tune, and evaluate foundation models for sequence/structure-to-property prediction and biologics design

Integrate multimodal data, including protein sequence, structure, imaging, assay, and experimental metadata, to improve predictive performance and model robustness

Design active learning, uncertainty-aware, or Bayesian optimization strategies to prioritize experiments and guide data generation across modalities

Build reproducible modeling workflows and evaluation frameworks that support rigorous model comparison, validation, and deployment-readiness

Collaborate cross-functionally with scientists and software engineers to translate machine learning innovation into actionable insights for biologics discovery and optimization

Communicate scientific findings and modeling strategies through presentations, technical documentation, and cross-functional forums

What we expect of you

We are all different, yet we all use our unique contributions to serve patients. The collaborative professional we seek is a Senior Data Scientist with these qualifications.

Basic Qualifications

Doctorate degree with 4+yrs in Data Science, Computer Science, Computational Biology, Bioinformatics, Computational Chemistry, or a related field

Or

Master's degree and 8+ years of directly related experience

Preferred Qualifications

Experience developing machine learning models for biological, protein engineering, drug discovery or immunology applications

Strong proficiency in Python and experience with modern deep learning frameworks such as PyTorch

Experience with Python scientific computing tools, such as numpy, scipy, pandas, etc.

Experience with foundation models, representation learning, sequence-to-property modeling, and multimodal learning

Experience integrating sequence, structural, assay, imaging, and metadata sources into reliable, model-ready datasets

Proven ability to apply active learning, Bayesian optimization, uncertainty quantification and model interpretation to scientific problems

Experience working with large-scale datasets and in high-performance computing environments, including cloud-based platforms (e.g., AWS)

Familiarity with reproducible machine learning and software engineering practices, including version control (git), testing, containerization (Docker), workflow orchestration, data versioning, or experiment tracking.

Strong scientific communication skills, with publications in leading ML, computational biology, or protein science venues such as NeurIPS, ICML, ICLR, ISMB, Nature Biotechnology, Nature Methods, Cell Systems, MABS, PNAS or comparable conferences and journals; candidates should highlight representative publications on their resume.

.