Senior Data Scientist

KEY RESPONSIBILITIES

Develop and apply machine learning models to predict sequence-to-property relationships.

Develop scalable, reproducible data-processing, feature-generation, model-training, and evaluation workflows for large-scale biological datasets.

Design, implement and train state-of-the-art foundation models including protein language models, diffusion models, or other generative approaches using multimodal biological data.

Develop interpretable and uncertainty-aware modeling approaches that quantify confidence and support decision-making.

Apply active learning and Bayesian optimization to guide experimental design and iterative improvement of protein design workflows.

Collaborate cross-functionally with experimental teams to define modeling objectives, prioritize experiments, and interpret results.

Communicate scientific findings clearly through presentations, written summaries and discussions with technical and non-technical stakeholders.

What we expect of you

We are all different, yet we all use our unique contributions to serve patients. The dynamic 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 predictive machine learning models for biological, structural, biophysical, or experimental outcome data.

Strong programming skills in Python and experience with modern machine learning frameworks such as PyTorch

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

Experience with protein language models, representation learning, transfer learning, and generative modeling.

Experience with uncertainty estimation, model validation, calibration, and generalizability assessment across experimental conditions.

Familiarity with active learning, Bayesian optimization, design-of-experiments, or closed-loop experimental workflows.

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.

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