Build and operate scalable scientific data pipelines and services integrating laboratory, OMICS, imaging, assay, formulation, consumer, and performance data. Implement metadata, provenance, versioning, identity resolution, access, semantic, and data-quality controls. Develop interfaces connecting repositories, knowledge graphs, analytical tools, computational models, and AI workflows. Monitor reliability, availability, dependencies, and performance while resolving bottlenecks and providing operational support for trusted analytical data foundations.

Lead the design, evaluation, deployment, and scaling of predictive Bio-AI models, multi-agent scientific workflows, RAG systems, and LLM applications. Build evaluation frameworks and production controls for model versioning, observability, monitoring, CI/CD, access control, auditability, and human review. Partner with scientists, bioinformaticians, and data engineers to transform scientific workflows into reliable AI capabilities and improve them through experimental feedback.

Develop AI-driven methods for skin and scalp biology discovery, integrating multimodal biological datasets to generate predictive insights. Build Design-Build-Test-Learn workflows, analyze imaging, omics, phenotypic, screening, and experimental data, and collaborate with experimental scientists to translate biological questions into computational models. Contribute to an AI-enabled discovery platform supporting autonomous and predictive scientific research.