Senior Data Scientist - Protein Structure ML models

Career Category Research Job Description Senior Data Scientist - Protein Structure ML models Role Summary The Senior Data Scientist - Protein Structure ML models will play a critical role in advancing AI-enabled protein and antibody design across Large Molecule Discovery (LMD). This role will focus on building, adapting, and validating machine learning models that predict protein function from structure, with particular emphasis on antibodies and antibody-like molecules. Working at the intersection of machine learning, structural biology, protein engineering, and experimental discovery, this individual will also develop workflows that combine structure prediction, structure generation, and inverse-folding models into practical protein design pipelines. The role will partner closely with wet-lab scientists to guide assay design, generate high-value property data, and translate internal and externally available datasets into rigorous validation strategies. This role is ideal for someone who enjoys developing technically rigorous geometric ML methods while remaining deeply connected to experimental validation and real-world biologics discovery needs.

Protein Structure to Function Modeling Build machine learning models, either developed in-house from scratch or adapted from existing examples, to predict protein function from structure. Focus model development on antibodies and antibody-like molecules, including formats that require structure-aware modeling approaches for downstream property prediction. Use internal and externally available structural, sequence, binding, and property datasets to evaluate and improve model performance. Antibody Binding Model Validation Design, own, and maintain validation tasks for assessing antibody binding models across internal and externally available datasets. Establish well-curated benchmarks that support quality control, model comparison, and responsible onboarding of external / open-source ML models. Translate validation results into actionable guidance for model selection, model improvement, and downstream protein design decisions. Experimental Data Generation Partnership Partner with wet-lab scientists and structural biologists to guide assays for collecting property data that can improve model training, validation, and decision-making. Help define data collection strategies that connect experimental readouts to ML model objectives and biologics design hypotheses. Work cross-functionally to interpret experimental outcomes and incorporate learnings into iterative model development workflows. Integrated Protein Design Workflows Develop workflows that chain or efficiently combine structure prediction, structure generation, and inverse-folding models for protein design. Apply structure-aware ML tools to support de novo design and optimization of antibodies and related formats. Contribute reusable workflows, validation practices, and technical standards that improve scalability and reproducibility of protein design pipelines.

Bachelor’s degree in Computational Biology, Bioinformatics, Life Sciences, Computational Chemistry, Chemical Engineering, Materials Science, Data Science, or a related quantitative field and relevant professional experience.

Requirements Bachelor’s degree and 6+ years of relevant experience, OR Master’s degree and 4+ years of relevant experience, OR PhD · Preferred Qualifications Programming and ML Frameworks Fluent in Python and able to develop reliable, reusable scientific software for model development, data analysis, and workflow automation. Hands-on experience with at least one modern deep learning framework, such as PyTorch or JAX. Ability to work across research codebases, adapt existing implementations, and mature promising examples in the literature into robust internal workflows. Protein Structure Modeling Strong foundation and hands-on experience with protein structure modeling, particularly