Senior Scientist II
About The Position
Exelixis is seeking a Senior Scientist II in Computational Chemistry to join its small-molecule discovery organization. The role applies physics-based molecular modeling, cheminformatics, and data-driven methods to advance drug discovery programs, connecting molecular simulation with cheminformatics, workflow automation, and practical machine learning. The scientist will contribute to molecular dynamics, free-energy calculations, structure-based design, and compound prioritization, while developing reliable computational and machine-learning workflows to increase the impact of predictive methods across the discovery portfolio. The position works closely with medicinal chemists, structural biologists, biologists, pharmacologists, bioinformatics scientists, and other stakeholders in a fast-paced, interdisciplinary research environment. It calls for scientific judgment, programming ability, clear communication, and effective collaboration. The role is based in Alameda, California. The office is described as a modern, collaborative space, with equipment including dual monitors and ergonomic chairs.
Requirements
- Bachelor’s degree in a related discipline and at least 9 years of relevant experience; or a master’s degree in a related discipline and at least 7 years of relevant experience; or a Ph.D. in Computational Chemistry, Cheminformatics, or a related discipline with at least 2 years of relevant experience in the biotechnology or pharmaceutical industry; or an equivalent combination of education and experience.
- Strong foundation in molecular modeling and the physical principles governing protein-ligand interactions.
- Hands-on experience with one or more structure-based drug-design methods, such as molecular docking, molecular dynamics, free-energy calculations, conformational analysis, or protein-ligand interaction analysis.
- Proficiency in Python for scientific computing, data analysis, or computational workflow development.
- Working knowledge of cheminformatics concepts, such as molecular representations, fingerprints, molecular descriptors, similarity searching, substructure searching, clustering, or compound-library analysis.
- Familiarity with open-source cheminformatics or molecular-modeling tools such as RDKit, OpenMM, GROMACS, AMBER, or comparable platforms.
- Ability to critically analyze computational and experimental data and translate results into clear, testable molecular-design hypotheses.
- Understanding of structure-activity relationships and physicochemical characteristics relevant to small-molecule drug discovery.
- Strong written, oral, and presentation communication skills, and ability to work effectively in cross-functional teams and communicate findings to scientists from different technical backgrounds.
- Scientific curiosity and willingness to learn new methods as program needs and technologies evolve.
- Strategic thinking and interest in applying computational and machine-learning approaches to practical drug-discovery problems.
- Collaborative, proactive, innovative, and adaptable approach in a fast-paced environment, with a balance of technical depth and project impact orientation.
Responsibilities
- Apply ligand- and structure-based computational methods to support small-molecule discovery programs.
- Plan, execute, analyze, and communicate molecular dynamics simulations and free-energy calculations for compound prioritization and molecular design.
- Use molecular docking, protein-ligand interaction analysis, conformational analysis, similarity searching, and other computational approaches to develop experimentally testable design hypotheses.
- Use cheminformatics to analyze compound collections, explore structure-activity relationships, assess chemical diversity, and support virtual library design and prioritization.
- Evaluate and apply machine-learning methods for molecular property or activity prediction alongside physics-based modeling and experimental data.
- Assess machine-learning model performance and applicability using appropriate validation methods, and communicate limitations and uncertainty.
- Apply and develop cheminformatics and machine-learning models to enable predictive analytics across the pipeline.
- Contribute to virtual screening campaigns using structure-based, ligand-based, and fragment-based approaches.
- Integrate computational results into collaborative discovery platforms and project workflows, including LiveDesign and Spotfire.
- Collaborate with medicinal chemists, structural biologists, biologists, pharmacologists, and other project stakeholders to interpret data and inform compound-design decisions.
- Present computational analyses, recommendations, assumptions, and uncertainties clearly to scientific project teams.
- Stay current with emerging methods in molecular simulation, free-energy calculations, cheminformatics, machine learning, and computer-aided drug design.
- Contribute to the development of computational chemistry capabilities and a predict-first culture within the discovery organization.
Benefits
- 401(k) plan with generous company contributions
- Group medical coverage
- Dental coverage
- Vision coverage
- Life insurance
- Disability insurance
- Flexible spending accounts
- Eligibility for a discretionary annual bonus program, or a sales-based incentive plan for field sales staff
- Opportunity to purchase company stock
- Long-term incentives
- 15 accrued vacation days in the first year
- 17 paid holidays, including a company-wide winter shutdown in December
- Up to 10 sick days throughout the calendar year
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What This Job Offers
Job Type
Full-time
Career Level
Senior
