Senior Computational Chemist - Oncology Targeted Discovery
About The Position
The Senior Computational Chemist will use structure-based design, ligand-based approaches, and modern AI/ML to guide high-impact oncology discovery programs across new modalities. The role contributes to the design and selection of targeted medicines, translating complex data into decisions and influencing strategy from early ideas through candidate selection. Working at pace with colleagues across medicinal chemistry, biology, biophysics, and other disciplines, the successful candidate will help shorten design cycles, increase confidence in decisions, and advance promising molecules. The position involves generating testable design hypotheses that integrate structural insights, SAR, and biophysical data; applying computational methods to prioritize compounds for potency, selectivity, and developability; and building, validating, and deploying predictive models. It also includes developing computational methods for antibody–drug conjugates and bifunctional proximity-inducing agents, collaborating across functions, communicating scientific results, and contributing reusable methods and workflows that improve computational chemistry across programs. The role is based on an in-person working model, with an average minimum of three days per week in the office and flexibility considered. The annual base-pay range is $124,713.60–$187,070.40. The position is eligible for short-term incentive bonuses and equity-based awards for salaried roles. Benefits include qualified retirement programs, paid time off (including vacation, holidays, and leaves), and health, dental, and vision coverage, subject to applicable plan terms.
Requirements
- Ph.D. in computational chemistry or a related field.
- At least 1 year of experience.
- Deep knowledge in one or more areas of computational chemistry, such as quantum mechanics, molecular dynamics, structure-based drug design, or ligand-based drug design.
- Experience with AI/ML software toolkits for modeling chemical and biological data, such as RDKit, ChemProp, DeepChem, PyTorch, and scikit-learn.
- Excellent problem-solving skills.
- A proven track record of scientific publications and presentations.
Responsibilities
- Generate testable design hypotheses integrating structural insights, SAR, and biophysical data to guide medicinal chemistry and accelerate decision-making.
- Apply docking, pharmacophore modeling, and data-driven design to prioritize compounds for improved potency, selectivity, and developability.
- Build, validate, and deploy predictive AI/ML models using contemporary toolkits to support high-throughput experiment design and improve hit-to-lead success.
- Develop and refine computational methods for antibody–drug conjugates and bifunctional, proximity-inducing agents.
- Collaborate with medicinal chemistry, biology, biophysics, DMPK, and safety sciences to align models with mechanistic understanding and project goals.
- Present clear scientific results internally and externally, contribute to publications, and support the organization’s scientific reputation.
- Deliver project acceleration and contribute reusable methods, workflows, and best practices across computational chemistry programs.
Benefits
- Eligibility for short-term incentive bonuses.
- Equity-based awards for salaried roles.
- Qualified retirement programs.
- Paid time off, including vacation, holidays, and leaves.
- Health coverage.
- Dental coverage.
- Vision coverage.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Ph.D. or professional degree
