Scientist, Discovery Cheminformatics
Octant Bio
Summary
Octant is seeking a scientist with a strong background across the chemical and computational domains. You will apply established and emerging cheminformatics and machine learning methods to drive Octant's small molecule drug discovery programs. This is a unique opportunity to leverage and shape our iterative, direct-to-biology chemical synthesis platform and expansive datasets, all in the context of live programs.
Job description
In this interdisciplinary role, you will engage the chemistry and biology of our programs through a computational lens. You are motivated by the mission of Octant and have a team-first mindset. You bring excellent communication skills and an ability to thrive in a dynamic and fast-paced environment.
In this role, you will:
- Lead cheminformatics on multiple drug discovery programs, analyzing data with medicinal chemists and biologists each week and shaping which compounds get made.
- Expand Octant's cheminformatics and ML capabilities, including methods for iterative exploration of chemical space, development of predictive ADME models and scoring functions, and help set our long-term computational strategy as the field moves.
- Collaborate with chemists to design and test chemical libraries on our nanoscale and microscale synthesis platform, guiding hit-to-lead and lead optimization.
- Develop and contribute to the data and computational infrastructure, enabling scientists to access and analyze their own data in consistent, statistically rigorous ways.
- Increase scientist leverage by building the agentic layer of our discovery workflows: encode cheminformatics methods as tools that AI agents use to propose, triage, and analyze compounds alongside program scientists with the appropriate strategies and safeguards for decision making.
This job might be great for you if you have:
- An advanced degree (preferably a PhD) in chemistry, computer science, statistics, or a related field.
- 2+ years of drug discovery experience, academic or industry.
- Working knowledge of medicinal chemistry: you can reason about structure-property relationships and be fluent chemically in discussions with chemists.
- Deep cheminformatics expertise (SAR analysis, library enumeration, property calculation) and fluency with the standard toolkits (e.g. RDKit, Datamol, OEChem).
- Applied machine learning to chemical problems, including QSAR or property models that were deployed and used on live programs; comfort with the relevant stack (e.g. PyTorch, JAX, scikit-learn, DeepChem).
- Experience working side-by-side with medicinal and computational chemists, data scientists, and engineers, including influencing which molecules get made; an instinct for building analyses and tools that scientists can run themselves.
- Strong software skills: fluent in Python and its data stack (NumPy, pandas, Polars), experienced with cloud infrastructure, databases, and version control; writes reproducible analyses that other people can run and build on.
- Familiarity with structure-based computational chemistry methods (protein structure analysis, docking, MD) and interest in going deeper.
- Experience curating public chemical datasets (e.g. ChEMBL, PubChem) and using them to augment internal models.
- Built tooling and infrastructure for agentic workflows.
Requirements
- An advanced degree (preferably a PhD) in chemistry, computer science, statistics, or a related field.
- 2+ years of drug discovery experience, academic or industry.
- Working knowledge of medicinal chemistry: you can reason about structure-property relationships and be fluent chemically in discussions with chemists.
- Deep cheminformatics expertise (SAR analysis, library enumeration, property calculation) and fluency with the standard toolkits (e.g. RDKit, Datamol, OEChem).
- Applied machine learning to chemical problems, including QSAR or property models that were deployed and used on live programs; comfort with the relevant stack (e.g. PyTorch, JAX, scikit-learn, DeepChem).
- Experience working side-by-side with medicinal and computational chemists, data scientists, and engineers, including influencing which molecules get made; an instinct for building analyses and tools that scientists can run themselves.
- Strong software skills: fluent in Python and its data stack (NumPy, pandas, Polars), experienced with cloud infrastructure, databases, and version control; writes reproducible analyses that other people can run and build on.
- Familiarity with structure-based computational chemistry methods (protein structure analysis, docking, MD) and interest in going deeper.
- Experience curating public chemical datasets (e.g. ChEMBL, PubChem) and using them to augment internal models.
- Built tooling and infrastructure for agentic workflows.
Benefits
- Health insurance
- Dental insurance
- Vision insurance
- Equity
- 401(k) matching
Company overview
Octant Bio is a biotechnology company founded in 2017 and headquartered in Emeryville, California, that specializes in developing next-generation small molecule therapeutics for complex diseases. The company combines advanced cell engineering, high-throughput chemistry, and automation to engineer best-in-class drugs targeting rare diseases, oncology, and immunology. Octant's innovative Navigator platform utilizes synthetic biology, high-throughput experimentation, and AI/ML data analysis to understand and modulate how target proteins interact within cells, rather than just binding molecules to isolated proteins. This approach enables the company to tackle protein misfolding diseases that have proven intractable with conventional methods. With a team of experienced technologists and entrepreneurs, Octant has raised significant funding, including an $80 million Series B round, and is working on promising programs in rare disease and oncology. The company's mission focuses on scaling drug discovery through massive experimentation in human cells to unlock novel insights into disease mechanisms.