Principal Scientist, Computational Chemistry

About the role

The role owns the end-to-end computational chemistry strategy for key oncology and rare disease programs, translating biological complexity into tractable molecular hypotheses. You will own the design and execution of structure-based and data-driven projects that de-risk lead optimization decisions and accelerate candidate selection. This position owns the development and governance of predictive modeling frameworks that directly inform medicinal chemistry priorities and project timelines. You will own mentorship of scientific partners, elevating the collective capability to interpret and act on computational insights. This role owns accountability for high-quality scientific communication across internal and external stakeholders. You will own the stewardship of robust, reproducible workflows that ensure integrity and scalability of computational outputs. This position drives innovation in computational methodologies to maintain a competitive edge in target classes with high clinical unmet need. You will own the integration of emerging technologies to strengthen the overall drug discovery engine.

Key facts

What you'll do

• Apply structure-based and ligand-based drug design methods - including docking, protein modeling, and a range of computational techniques - to support projects from hit discovery through lead optimization.
• Collaborate with project team members, engaging them in iterative structure-based drug design cycles to generate hypotheses, evaluate targets, triage hits, and guide SAR exploration.
• Present modeling insights clearly and effectively to cross-functional project teams, ensuring data is interpreted in the right context.
• Develop, refine, and maintain robust computational workflows on HPC Linux environments, enabling scalable execution of large-scale simulations.
• Train and mentor colleagues on computational chemistry tools, cheminformatics standards, data formats, and key workflow practices.
• Build and validate predictive models that translate complex biological data into actionable structural hypotheses for lead optimization.
• Partner with experimental teams to align computational priorities with biological and assay constraints, ensuring efficient resource allocation.
• Champion the adoption of cheminformatics standards and molecular representations to streamline data integration and reproducibility.
• Evaluate and recommend new computational tools, platforms, and infrastructure to support evolving project needs and scalability.
• Lead focused project reviews where data-driven recommendations guide strategic decisions on compound progression and optimization pathways.
• Translate project objectives into tailored computational strategies that balance innovation with practical development timelines.
• Contribute to internal scientific discussions and publications, reinforcing Cogent Biosciences' scientific leadership in targeted therapies.

Requirements

• Ph.D. in Computational Chemistry or a related field, with 3+ years of relevant industrial experience.
• Expertise in structure-based design, docking, virtual screening, pharmacophore modeling, and other computational approaches for lead optimization.
• Expertise in MOE modeling software, with working knowledge of other commercial and open-source computational chemistry platforms preferred.
• Strong hands-on experience with cheminformatics platforms and tools, including Spotfire.
• Strong programming skills with experience in building custom scripts or automated pipelines, is a plus.
• Proficiency in Linux/UNIX, shell scripting, and HPC frameworks (e.g., SLURM, LSF, PBS/Torque).
• Experience optimizing large computational workloads on cluster or cloud infrastructure.
• Familiarity with molecular representations and cheminformatics standards (SMILES, SDF, descriptors, fragment-based encodings).
• A solid publication record and excellent scientific communication skills.
• Proven effectiveness working within cross-functional, data-driven scientific teams.

Nice to have

• Prior experience in oncology or rare diseases with high-need therapeutic targets is preferred.
• Exposure to kinase targets and related signaling pathways is a preferred qualification.
• Experience with automated experimental feedback loops or AI-driven discovery platforms is valued.

Practical notes

• This is a full-time, on-site position based at our R&D facility in Boulder, Colorado.
• Travel may be required for scientific meetings, cross-site collaboration, or regulatory engagements as needed.
• Candidates must meet all eligibility requirements as stated, and no exceptions will be made for items not explicitly listed.
• The compensation range provided reflects target values and may be adjusted within the stated band based on candidate qualifications and location.
• Exact compensation, including bonus and equity components, will vary based on skills, experience, and location.
• Cogent Biosciences is an equal opportunity employer and welcomes diverse candidates to apply.