Scientist, Discovery Cheminformatics

Octant Bio•Emeryville, CA

1d•$145,000 - $185,000

About The Position

Octant is seeking a scientist with strong expertise across chemical and computational disciplines to apply established and emerging cheminformatics and machine learning methods to small-molecule drug discovery programs. The role offers the opportunity to shape and use Octant’s iterative, direct-to-biology chemical synthesis platform and extensive datasets in active discovery programs. Working across chemistry and biology from a computational perspective, the scientist will lead cheminformatics activities for multiple programs, develop computational and machine-learning capabilities, collaborate on chemical library design and optimization, and build data infrastructure and agentic workflows that help scientists make and evaluate decisions. The position calls for a motivated, collaborative scientist with excellent communication skills who can thrive in a dynamic, fast-paced environment. Octant is a clinical-stage drug discovery company developing correctors for protein-misfolding diseases, with active programs in rare diseases and oncology. Its Navigator platform combines high-throughput synthetic biology, generative chemistry, and AI/ML to discover and develop therapeutics against complex cellular mechanisms in human cells. The platform brings together biological measurements, nanoscale chemical synthesis, and machine learning to support iterative drug-program progress.

Requirements

Advanced degree in chemistry, computer science, statistics, or a related field; a PhD is preferred.
At least 2 years of drug discovery experience in academia or industry.
Working knowledge of medicinal chemistry, including reasoning about structure-property relationships and communicating fluently with chemists.
Deep cheminformatics expertise, including SAR analysis, library enumeration, and property calculation, with fluency in standard toolkits such as RDKit, Datamol, or OEChem.
Applied machine learning to chemical problems, including QSAR or property models deployed and used on live programs, and familiarity with relevant tools such as PyTorch, JAX, scikit-learn, or DeepChem.
Experience working closely with medicinal and computational chemists, data scientists, and engineers, including influencing which molecules are made and building analyses and tools scientists can run themselves.
Strong software skills, including fluency in Python and its data stack (NumPy, pandas, Polars), experience with cloud infrastructure, databases, and version control, and the ability to write reproducible analyses others can run and build on.

Nice To Haves

Familiarity with structure-based computational chemistry methods, including protein structure analysis, docking, or molecular dynamics, and interest in developing further expertise.
Experience curating public chemical datasets such as ChEMBL or PubChem and using them to augment internal models.
Experience building tooling and infrastructure for agentic workflows.

Responsibilities

Lead cheminformatics for multiple drug discovery programs, analyzing data weekly with medicinal chemists and biologists and helping determine which compounds are made.
Expand Octant’s cheminformatics and machine-learning capabilities, including methods for iterative chemical-space exploration, predictive ADME models, and scoring functions, and help shape the long-term computational strategy.
Collaborate with chemists to design and test chemical libraries on nanoscale and microscale synthesis platforms, supporting hit-to-lead work and lead optimization.
Develop and contribute to data and computational infrastructure that enables scientists to access and analyze data consistently and with statistical rigor.
Build the agentic layer of discovery workflows by encoding cheminformatics methods as tools that AI agents can use to propose, triage, and analyze compounds alongside program scientists, with appropriate decision-making strategies and safeguards.