About the role
We’re looking for a mid-level computational chemist to join our discovery team and contribute to live hit-to-lead and lead optimization projects. You’ll play a key role in connecting in silico modeling with medicinal chemistry to design better compounds across a wide range of targets. Beyond hands-on modeling, you’ll be expected to take an active role in shaping project strategies, planning modeling efforts, and influencing prioritization decisions based on data.
Key responsibilities
- Use molecular modeling tools (e.g., docking, molecular dynamics, FEP, pharmacophore design) to support compound design
- Interpret SAR data, suggest new analogs, and prioritize compound ideas
- Collaborate with medicinal chemistry teams
- Contribute to planning project workflows and setting modeling priorities
- Drive hypothesis generation and test cycles based on evolving data
- Apply both structure- and ligand-based design strategies
- Contribute to data-driven iteration and project decision-making
Required Qualifications
- Ph.D. in Computational Chemistry, Medicinal Chemistry, or a related field with 2–5 years of experience in drug discovery
– OR –
Master’s degree with 5+ years of hands-on industry experience in computational drug discovery or related roles
- Proven track record of contributing to hit identification, hit-to-lead, or lead optimization programs
- Proficiency in molecular modeling platforms
- Strong understanding of medicinal chemistry and SAR interpretation
- Comfortable working in fast-moving, cross-functional project teams
Preferred Qualifications
What we offer
- Drive real drug discovery with visible impact on pipeline progress
- Join a science-first team blending AI and chemistry for next-gen therapeutics
- Enjoy flexible working with support for remote collaboration
We're looking for someone who is:
Is actively contributed to real drug discovery projects and can confidently apply modeling techniques to impact design decisions. Is comfortable balancing scientific depth with practical prioritization and can independently plan and drive modeling strategies within multi-disciplinary projects.
