Senior Scientist II (Computational Chemistry)

Exelixis•Alameda, CA

5d•$149,000 - $188,000•Onsite

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, with an emphasis on connecting molecular simulation with cheminformatics, workflow automation, and practical machine learning. The scientist will contribute to molecular dynamics, free-energy calculations, structure-based design, compound prioritization, and the development of reliable computational and machine-learning workflows. The role works closely with medicinal chemists, structural biologists, biologists, pharmacologists, and bioinformatics scientists in a fast-paced, interdisciplinary research environment. Strong scientific judgment, programming ability, communication, and collaboration skills are essential.

Requirements

Bachelor’s degree in a related discipline and a minimum of 9 years of relevant experience; or a master’s degree in a related discipline and a minimum of 7 years of relevant experience; or a PhD 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.
Ability to analyze computational and experimental data critically 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.
Ability to work effectively in cross-functional teams and communicate computational findings to scientists from different technical backgrounds.
Scientific curiosity and willingness to learn new methods as program needs and technologies evolve.

Nice To Haves

Familiarity with open-source cheminformatics or molecular-modeling tools such as RDKit, OpenMM, GROMACS, AMBER, or comparable platforms.
Strategic thinking and enthusiasm for applying computational and machine-learning approaches to practical drug-discovery problems.
Collaborative approach and ability to work across disciplines.
Proactive, innovative, and adaptable approach in a fast-paced environment.
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.
Apply molecular docking, protein-ligand interaction analysis, conformational analysis, similarity searching, and other computational approaches to generate experimentally testable design hypotheses.
Use cheminformatics methods to analyze compound collections, explore structure-activity relationships, assess chemical diversity, and support virtual library design and prioritization.
Evaluate and apply appropriate machine-learning methods for molecular property or activity prediction, combining them with physics-based modeling and experimental data.
Assess machine-learning model performance and applicability using scientifically 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 tools such as LiveDesign and Spotfire.
Collaborate with medicinal chemists, structural biologists, biologists, pharmacologists, and other project stakeholders to interpret data and influence 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 continued development of computational chemistry capabilities and a predict-first culture within the discovery organization.

Benefits

401(k) plan with generous company contributions.
Group medical, dental, and vision coverage.
Life and 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.