The Opportunity: Client is seeking a hands-on Contract AI/ML scientist to accelerate project work focused on TCI-ability using protein structure, phenotypic, and multimodal data. This individual contributor will partner closely with computational biology, data science, translational, and discovery teams to develop, evaluate, and apply machine-learning approaches that turn diverse biological datasets into actionable project insights. The role is time-bound and delivery-oriented, with a strong emphasis on building robust analyses, prototypes, and reusable workflows for defined program needs. Responsibilities: Develop and apply machine-learning and statistical approaches for defined TCI-ability projects using protein structure data, phenotypic data, and other multimodal biological datasets. Curate, harmonize, and quality-control structured and unstructured data from internal and external sources; document data provenance, assumptions, and limitations. Build reproducible computational workflows for feature generation, model training, validation, and performance evaluation. Integrate protein structural representations with cellular, phenotypic, and other biological modalities to generate testable hypotheses and prioritize follow-up analyses. Implement clear, maintainable analysis code and contribute to shared repositories, technical documentation, and handoff materials. Collaborate with cross-functional scientists to translate biological questions into tractable computational workplans, milestones, and decision-ready outputs. Communicate methods, findings, risks, and recommendations clearly to technical and non-technical stakeholders. Support rapid iteration on project-specific prototypes and analyses in a dynamic discovery environment. Required Skills, Experience and Education: Advanced degree (M.S. or Ph.D.) in computational biology, bioinformatics, computational chemistry, cheminformatics, computer science, data science, biophysics, quantitative biology, or a related field. Demonstrated hands-on experience applying machine learning to biological, chemical, or biomedical data. Experience working with protein structure data and/or structure-derived features in a research or drug discovery setting. Experience integrating, analyzing, or modeling phenotypic and multimodal datasets. Strong programming skills in Python and experience with common scientific computing and machine-learning libraries. Ability to build reproducible, well-documented computational workflows and clearly evaluate model performance. Strong problem-solving, written communication, and cross-functional collaboration skills. Ability to independently execute defined project work and deliver high-quality outputs on an agreed timeline. Preferred Skills: Experience in oncology, drug discovery, translational research, or pharmaceutical/biotechnology R&D. Experience with deep learning methods for protein structure, molecular, imaging, or other high-dimensional biological data. Familiarity with structural bioinformatics tools, protein representation learning, or structure-based modeling. Experience with cloud computing, workflow orchestration, version control, and collaborative software-development practices. Prior experience delivering computational analyses or prototypes in a contract, consulting, or fast-paced project environment.
Not specified in the original listing.
Not specified in the original listing.
