Reactivity Landscape Scientist: Predictive Modeling for Reactions

Eli Lilly and Company; South San Francisco (CA); On-site; USD 168,000 - 268,000; Full time

Job summary

Eli Lilly and Company is seeking a senior scientist to own the modeling and analysis of a quantitative reactivity landscape. You will work at the interface of quantum chemistry, machine learning, and experimental data to turn HT E and condition data into actionable models for chemists.

Based in the South San Francisco lab, you will collaborate with chemists operating automation platforms, driving model quality and interpreting analytical readouts to guide experimental campaigns.

Qualifications

• Ph.D. in Chemistry or related field (or MS with 3 years / BS with 5 years experience).
• Experience modeling and analyzing reaction data with rigorous validation and uncertainty handling.

Responsibilities

• Build and validate models that predict reaction outcomes and related metrics from substrate, catalyst and conditions.
• Analyze HT E datasets end to end: representation choice, feature engineering and cross‑validation design.
• Turn plate-level outputs into structured, model-ready reaction records with consistent condition encodings.
• Identify under-sampled regions of condition space and specify experiments to reduce uncertainty.
• Detect reactivity cliffs and mechanistic regime changes in data instead of smoothing them away.

Skills

Quantum chemistry

Machine learning

Python programming

Uncertainty quantification

Experiment-data analysis

Education

Ph.D. in Chemistry or related field

M.S. + 3 years / B.S. + 5 years experience

Tools

RDKit

scikit-learn

Pandas

PyTorch

Job description

Eli Lilly and Company is seeking a senior scientist to own the modeling and analysis of a quantitative reactivity landscape. You will work at the interface of quantum chemistry, machine learning, and experimental data to turn HT E and condition data into actionable models for chemists.

Based in the South San Francisco lab, you will collaborate with chemists operating automation platforms, driving model quality and interpreting analytical readouts to guide experimental campaigns.