Advisor​/Sr. Advisor - Computational Imaging Specialist

Position: Advisor / Sr. Advisor - Computational Imaging Specialist
At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve.

This is hard, urgent, selfless work-but it's work worth doing. If you're driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

** Position Purpose*
* This hands-on scientific role sits at the intersection of computational biology, quantitative image analysis, AI-assisted scientific computing, enterprise data science, and drug discovery, within an enterprise capability serving Lilly's therapeutic areas (diabetes, obesity, cardiometabolic, cardiorenal, women's health, neuroscience, immunology, and others). Operating independently as a scientific and technical domain expert, you will design, validate, and deploy enterprise-scale, cloud-based computational imaging solutions, emphasizing reproducibility over desktop workflows and the responsible use of AI, including AI coding assistants and agentic systems, that turn microscopy, histology, digital pathology, and spatial tissue analysis (Cellular neighborhoods, tissue organization, microenvironment features) data into decision-driving evidence from preclinical through translational and early clinical research.

Computational expertise in this role is complemented, not replaced, by hands-on wet-lab capability in brightfield IHC and IF workflows, grounding image analysis in practical understanding of staining, assay development, and the underlying biology. This is not a traditional bioinformatics, pathology, or standalone image-analysis position, and it does not involve radiology or medical imaging (CT, MRI, PET).

** Key Responsibilities*
* + Design, validate, and deploy quantitative image-analysis workflows for brightfield (histology, H&E, chromogenic, IHC) and fluorescence (IF, multiplex IF) imaging: segmentation, object detection, feature extraction, biomarker quantification, colocalization, and cell/tissue phenotyping.

+ Generate and evaluate IHC/IF samples at the bench as needed (tissue processing, staining, assay development, QC), applying this understanding of what drives image quality to ensure biologically meaningful analysis, solve workflows end to end, and collaborate effectively with bench scientists.

+ Build reproducible, scalable, cloud-based pipelines (AWS today; cloud-agnostic by design) with rigorous image QC, statistical analysis, and traceability.

+ Apply computer vision, machine learning, and deep learning, including foundation, multimodal, and generative models, to practical scientific problems; use AI coding assistants while rigorously validating AI-generated code and outputs.

+ Drive computational imaging strategy: translate scientific questions into scalable analytical solutions and deliver them to project achievements across therapeutic areas.

+ Evaluate commercial and open-source technologies, inform build-versus-buy decisions, and integrate vendor platforms (e.g., HALO, Visiopharm, Arivis Pro, Ato Mx) and open-source tools (e.g., QuPath) with internal Lilly capabilities.

+ Contribute to enterprise imaging data architecture: repositories, metadata, governance, and lineage.

+ Manage external collaborations and outsourcing with CROs, academic groups, and technology partners, including quality oversight of external data and analyses.

+ Contribute to biomarker discovery and validation, publications, and presentations; champion scientific software and responsible-AI protocols and mentor colleagues informally.

** Basic Qualifications*
* PhD (completed or imminent) in Computational Biology, Bioinformatics, Computer Science, Biomedical Engineering, Quantitative Biology, Data Science, or a related quantitative field

** Additional Preferences*
* + Demonstrated experience independently designing, developing, validating, and deploying computational image-analysis workflows.

+ Strong Python and R, with software engineering practices:
Git, code review, testing, reproducibility, documentation, and containerization (Docker preferred).

+ Demonstrated use of AI coding assistants and/or agentic AI systems, including critical review, validation, debugging, and quality control…