Reaction Informatics Scientist: Predictive Reactivity Landscape

BioSpace; South San Francisco (CA); On-site; USD 168,000 - 268,000; Full time

Job summary

Lilly is building an AI-enabled drug discovery lab in Silicon Valley, seeking a scientist to own the modeling and analysis layer of the Chemical Reactivity Landscape project. You will turn HTA data into predictive models guiding chemists on which conditions to run next, interfacing with an automation platform.

This role is primarily computational and lab-adjacent, onsite in South San Francisco, with responsibility across QM, ML, and analytical data quality.

Qualifications

• Ph.D. in Chemistry, Chemical Engineering, Computational or Physical Organic Chemistry, Cheminformatics, or a related field (or MS+3 years / BS+5 years of equivalent experience).
• Demonstrated experience modeling and analyzing reaction data — HTE, reaction condition, or reaction outcome datasets with rigorous validation and clear treatment of uncertainty

Responsibilities

• Build and validate models predicting reaction outcomes, yields, selectivity, conversions, and impurities.
• Analyze high-throughput experimentation data with feature engineering and cross-validation.
• Create structured, model-ready reaction records with consistent condition encodings.
• Characterize under-sampled regions of condition space and specify experiments to reduce uncertainty.
• Develop QM workflows and integrate descriptors with structural representations.

Skills

Quantum chemistry modeling

Machine learning

Python programming

Uncertainty quantification

Data analysis

Education

PhD in Chemistry or related field

Tools

RDKit

scikit-learn

Pandas

PyTorch

Job description

Lilly is building an AI-enabled drug discovery lab in Silicon Valley, seeking a scientist to own the modeling and analysis layer of the Chemical Reactivity Landscape project. You will turn HTA data into predictive models guiding chemists on which conditions to run next, interfacing with an automation platform.

This role is primarily computational and lab-adjacent, onsite in South San Francisco, with responsibility across QM, ML, and analytical data quality.