Lilly
Healthcare
Advisor-AntibodyDevelopabilityValidation&Benchmarking
Neural analysis suggests this role is
optimal for Senior candidates.
“Advisor - Antibody Developability Validation & Benchmarking at Lilly. Skills: Antibody developability, Model validation, Benchmarking, Federated learning. Build antibody developability benchmark suite. Define endpoint evaluation strategy”
What You'll Achieve.
Establish trust in federated antibody models; Triage real candidates; Contribute to model design choices; Deliver measurable lift over baselines; Simulate prospective deployment; Detect concept drift; Surface systematic failure modes; Communicate systematic biases; Communicate failure modes
Industry & Context.
Root cause analysis; Troubleshooting; Bias identification; Failure mode identification
Up to 10% travel
What They're Looking For.
Must Have
PhD in Computational Biology, PhD in Bioinformatics, PhD in Computer Science, PhD in related quantitative field, Publications on antibody developability prediction, Publications on model validation, Publications on benchmarking, Publications on reproducibility, Technical writing skills for partner-facing model cards, Technical writing skills for validation reports
Nice to Have
MLflow proficiency, Weights & Biases proficiency, Portfolio mindset balancing rigorous validation, Portfolio mindset balancing rapid deployment
What You'll Do.
Build antibody developability benchmark suite
Define endpoint evaluation strategy
Define multi-endpoint reliability roll-up
Architect privacy-preserving test set protocols
Design test set splitting strategies
Account for data asymmetry in test sets
Benchmark federated models against external resources
Characterize generalization gaps
Quantify federated training lift
Develop validation strategies across modalities
Develop validation strategies across formats
Implement temporal-split validation protocols
Implement sequence-similarity-aware validation protocols
Simulate prospective deployment
Surface systematic failure modes
Partner on architectural choices
Partner on feature choices
Partner on uncertainty quantification
Partner on calibration strategies
Partner on representation choices
Design statistically powered validation studies
Account for multiple testing
Account for hierarchical structure
Account for non-independent observations
Provide confidence intervals
Build MLOps pipelines
Ensure reproducibility of federated experiments
Version data snapshots
Version model checkpoints
Version hyperparameter configurations
Develop performance profiling
Identify systematic biases
Identify failure modes
Communicate findings to partners
Integrate validation frameworks with platform
Ensure scalable automated testing
How You'll Work.
Team & Collaboration
Partner with antibody modeling scientists; Collaborate with engineering teams
Communication Scope
Technical writing; Partner-facing reports
Process & Methodology
Portfolio mindset
Full Job Description
At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world. **O rganization Overview** At Lilly, we serve an extraordinary purpose. We make a difference for people around the globe by discovering, developing and delivering medicines that help them live longer, healthier, more active lives. Not only do we deliver breakthrough medications, but you also can count on us to develop creative solutions to support communities through philanthropy and volunteerism. **Purpose** Lilly TuneLab is an AI-powered drug discovery platform that provides biotech companies with access to machine learning models trained on Lilly's extensive proprietary pharmaceutical research data. Through federated learning, the platform enables Lilly to build models on broad, diverse datasets from across the biotech ecosystem while preserving partner data privacy and competitive advantages. Antibody developability prediction is a core workstream within TuneLab — covering aggregation, self-association, polyspecificity, thermal stability, viscosity, and chemical liabilities — that gates progression from discovery into lead optimization, cell line development, and formulation. The Advisor/Senior Advisor - Antibody Developability Validation & Benchmarking plays an essential role in establishing whether TuneLab's federated antibody models can be trusted to triage real candidates. The person in this seat must understand, at depth, how antibodies are characterized, what makes a sequence developable or not, and how predictions f
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