Product Scientist (Computational Chemist)

Inductive Bio

About The Position

Inductive Bio is an AI medicinal chemistry partner. We build virtual labs that help drug discovery teams make better decisions, faster, across the full DMTA cycle. Our platform includes Beacon-1 ADMET prediction models, human dose projection tools, and Indy, an AI medicinal chemistry assistant. Through a partnership with Enamine, we also offer library synthesis and direct-to-dose testing, closing the loop from computational design to experimental validation. Together, these power dozens of small molecule and beyond-rule-of-five programs for biotech and pharma partners. Beacon-1 placed 1st in both the 2025 Polaris and 2026 OpenADMET-ExpansionRx blind competitions, ahead of Merck & Co + NVIDIA and EMD Serono. We've raised $30M from Obvious Ventures, a16z Bio + Health, and Lux Capital, and were selected for a $21M ARPA-H CATALYST award to develop next-generation toxicity models. The Role We're seeking a seasoned computational or medicinal chemist to join the Product Science team as a Product Scientist. You'll be the primary Inductive scientific lead on partner programs, applying your med chem, comp chem, and DMPK expertise to real molecular optimization problems with a focus on dose-centric optimization. You'll work across a wide range of small molecule modalities, therapeutic areas, and modeling challenges. You'll also be a thought leader in the development of Inductive's platform, from our predictive models and dose projection tools to Indy, our AI medicinal chemistry assistant, informed by your own experience and what you see working (and not working) on real partner programs. We need someone whose instincts about what matters in small molecule optimization come from years of doing it, and who wants to put those instincts to work shaping products that empower drug discovery teams.

Requirements

A pragmatic drug discovery scientist with 5+ years on project teams across multiple stages of discovery, from hit identification through lead optimization. You've developed strong opinions about what works and what doesn't, and partners trust your judgment at the technical, scientific, and strategic level. Ideally you've seen a program through to DC nomination or IND-enabling studies.
Broad technical range across cheminformatics, structure/physics-based modeling, ML, and DMPK. We care more about your ability to pick the right tool for the problem than depth in any single technique. Deep scientific training in a relevant field is expected; the specific degree matters less than what you've done with it.
Comfortable writing Python and picking up new tools. We use Claude Code daily and prototype fast; strong coders with experience or interest in AI coding agents will thrive here. That said, we'll make exceptions for exceptional scientists who are genuinely eager to learn.
Clear communicator who can lead cross-functional discussions and turn complex analyses into visuals that land with both technical and non-technical audiences.

Responsibilities

Own the scientific relationship with partner project teams across modalities including reversible and covalent small molecules, macrocycles, molecular glues, and heterobifunctionals.
Bring order to messy program data. Rationalize SAR, identify what the data is and isn't telling you, and flag inconsistencies before they become bad decisions.
Guide med chem strategy: what to make, what to test, how to triage compounds for advancement, and where to focus synthetic resources. Identify liabilities early and help partners build de-risking plans.
Model in vivo outcomes end to end, from PK/PD/efficacy relationships to projected human dose, helping teams evaluate compounds through a dose-centric lens and reason about property tradeoffs in the context of therapeutic window.
Represent Inductive's scientific capabilities in partner meetings and publications.
Prototype scientific analyses and modeling experiments in code. We use Claude Code heavily within Product Science, so you can move from hypothesis to result quickly.
Build reusable analyses, tools, and workflows for internal use, for partner delivery, and for Indy. If you find yourself doing something twice, automate it.
Evaluate new computational methods on benchmark datasets and make pragmatic calls about what's worth adopting.
Dig into model performance issues. When predictions miss, understand why and work with the ML team to turn those findings into model improvements.
Stay current with the scientific literature and look for emerging techniques worth testing on real data.
Set scientific direction for the platform. You won't just provide feedback on what others build; you'll define what needs to exist and why, grounded in your past experience and what you see on partner programs.
Sit in product design meetings and own the user perspective. You use our tools every day; that makes you the most credible voice in the room for what's working and what isn't.
Write feature requirements, prioritize the roadmap alongside product and engineering, and hold the bar on whether what ships actually solves the problem.
Help grow the Product Science function as Inductive scales.