Advisor - Antibody Developability Validation & Benchmarking

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.Organization 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.PurposeLilly 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 from a federated model translate into go/no-go decisions in a discovery pipeline.This is a validation-led role that contributes to model design choices. The person will partner closely with antibody modeling scientists on architecture, feature design, and uncertainty quantification — not just downstream of them.Key ResponsibilitiesAntibody Developability Benchmark Suite: Build the canonical benchmark suite covering the full developability portfolio — aggregation propensity (AC-SINS, SMAC, CIC), thermal stability (nanoDSF/DSF), polyspecificity (BVP-ELISA, Heparin RT, PSR), self-interaction, viscosity, chemical liabilities (deamidation, isomerization, oxidation, N-glycosylation in CDRs), and immunogenicity surrogates. Define which endpoints are evaluated jointly versus independently and how multi-endpoint reliability rolls up to a triage decision.Sequence-Aware Federated Test Set Design: Architect privacy-preserving protocols for constructing representative test sets across distributed partner datasets, with splitting strategies appropriate to antibody data — germline-based, CDR-similarity-based, and clonotype-based splits that genuinely test generalization rather than near-duplicate memorization. Account for the structural asymmetry of antibody data (many sequences with shallow characterization, few sequences with deep characterization) when designing held-out evaluation sets.Public Benchmark Integration: Systematically benchmark federated antibody models against established external resources — SAbDab, OAS, TAP, the Jain et al. clinical-stage antibody panel, FLAb, and equivalent emerging datasets — to characterize generalization gaps and quantify where federated training delivers measurable lift over public-only baselines.Cross-Domain Validation: Develop validation strategies that assess model generalization across modalities and formats relevant to antibody developability — IgG vs. bispecific vs. fragment formats, different expression systems, different assay protocols across partners — while respecting partner data boundaries.Validation Frameworks: Implement temporal-split and sequence-similarity-aware validation protocols that simulate prospective deployment, detect concept drift as partner data accumulates, and surface systematic failure modes across CDR length distributions, germline families, and physicochemical regimes.Model Design Partnership: Work alongside antibody modeling scientists on architectural and feature choices that have direct validation implications — uncertainty quantification approaches, calibration strategies, structure-aware vs. sequence-only representations, and how predictions from different endpoints should be combined or kept independent.Statistical Rigor: Design statistically powered validation studies that account for multiple testing across endpoints, hierarchical structure in antibody data (sequences clustered by germline, project, partner), and non-independent observations. Provide honest confidence intervals on reported model performance.Reproducibility Infrastructure: Build robust MLOps pipelines ensuring complete reproducibility of federated experiments, including versioning of data snapshots, model checkpoints, and hyperparameter configurations.Performance Profiling: Develop comprehensive performance profiling across germline families, CDR length regimes, framework variants, and property ranges, identifying systematic biases and failure modes that should be communicated to partners.Platform Integration: Collaborate with engineering teams to integrate validation frameworks with the TuneLab federated learning platform built on NVIDIA FLARE, ensuring scalable and automated testing across the partner network.Basic QualificationsPhD in Computational Biology, Bioinformatics, Computational Chemistry, Computer Science, Statistics, or related field from an accredited college or universityMinimum of 4 years of post-PhD experience working with antibody discovery, engineering, or developability data in a biopharma