Director, Discovery Bioinformatics Oncology

Benefits

Additional Information

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. Job Summary: Lead the AI/ML innovation & deployment for oncology discovery. This role will architect and operationalize state‑of‑the‑art machine learning -including deep learning, foundation models, and LLM‑powered applications-to accelerate target identification & validation, protein and antibody design, and multimodal data integration across our discovery pipeline. Partnering closely with biology, chemistry, translational sciences and data hub, you'll transform heterogeneous molecular and phenotypic data into actionable hypotheses, design in silico-to-in vitro loops, and deliver decision‑quality insights that shape our discovery roadmap. This role also steers platformization efforts for in silico design & advancement of antibody, XDC development and next‑generation data products that scale across programs. Job Responsibilities: Innovate and execute the AI/ML strategy for discovery. Build a portfolio of models for target ID/validation, structure‑ and sequence‑based protein design (e.g., antibodies, conjugates), mode‑of‑action inference, and biomarker discovery. Establish retrieval‑augmented and agentic LLM workflows for knowledge mining (literature, patents, internal reports) and protocol/screen design assistance. Develop next‑gen data integration platforms. Integrate bulk & single‑cell transcriptomics, WES/WGS, proteomics, CRISPR screen data, imaging, functional readouts, and real‑world knowledge graphs into unified model‑ready datasets. Drive ontology/harmonization, feature stores, and model registries for reproducibility and added value extraction. Advance computational protein & antibody design. Leverage transformer‑based sequence models, diffusion/graph methods, and physics‑informed constraints for binder optimization, specificity, and developability; operationalize active‑learning loops with design-make-test cycles. Lead antibody-siRNA conjugate design heuristics and predictive models for delivery and efficacy. Design & oversee experiments (dry & wet). Plan benchmarking and prospective validation; pair ML predictions with targeted assays and orthogonal analytics. Build feedback loops to refine models with experimental results and post‑market learnings. Cross‑functional impact & leadership. Partner with Biology/Chemistry/Translational/Clinical Biomarkers to convert insights into program decisions. Represent computational strategy in steering committees and external partnerships; publish/present at top venues. Mentor and grow a high‑performing team (data scientists, ML engineers, bioinformaticians) with strong engineering and scientific rigor. Deliver robust, scalable ML systems. Own MLOps (data/feature pipelines, training/evaluation services, CI/CD, monitoring) on cloud (e.g., AWS) with containerization and orchestration (Docker/Kubernetes). Institute model governance: experiment tracking, versioning, bias/variance reporting, and validation SOPs. Foundational bioinformatics. Best‑practice omics analysis (RNA/DNA‑seq, single‑cell, proteomics), QC, and statistical analysis. Ensure data integrity, FAIR practices, to advance oncology drug discovery programs. Basic Requirements: PhD or MS in Computer Science, Computational Biology, Bioinformatics, Statistics, Applied Math, or related STEM field. 5+ years of post‑doctoral/industry experience delivering ML solutions in biotech/pharma or adjacent domains. Preferred Requirements: Experience in leading teams and cross‑functional initiatives is preferred. Demonstrated impact applying deep learning to biological problems (e.g., transformers for protein/antibody sequence, structure prediction/refinement, graph learning, diffusion models, transfer learning, multimodal integration). Deep hands‑on expertise with PyTorch (preferred) and/or JAX/TensorFlow; experience with Hugging Face (Transformers, Diffusers) and foundation‑model fine‑tuning (LoRA/PEFT, adapters, RAG). Track record building LLM applications (prompt engineering, tool use/agents, vector databases, retrieval pipelines) for knowledge extraction, hypothesis generation, and protocol design in drug discovery. Strong software engineering skills: Python, ML tooling (PyTorch Lightning, Hydra, Weights & Biases/MLflow), Git/GitHub, code review, testing & productionizing models with Docker/Kubernetes, APIs, and AWS services (e.g., S3, Batch/EKS, Lambda, Step Functions, SageMaker or equivalent). Solid grounding in statistics/causal inference/experimental desi