Data Scientist - Research Sovereign AI

Mayo Career Site US•Rochester, MN

About The Position

Data Scientist Foundational Model Science Position Summary The Data Scientist for Foundational Model Science is the senior technical leader, and the lead scientist responsible for designing, training, and governing Mayo’s multimodal foundational model. This model forms the core intelligence layer used by clinical departments, researchers, agentic workflows, and sovereign AI collaborations. The individual will work as a hands-on architect, model-builder, and researcher while acting as a player–coach, guiding strategy and building a future team.

Responsibilities

Design multimodal foundational model architectures integrating signals from imaging, text, waveforms, structured data, graph representations, and temporal embeddings.
Develop fusion, alignment, and cross-modal reasoning mechanisms (early fusion, late fusion, token-level fusion, hybrid models).
Define and implement methods for grounded clinical reasoning, retrieval-augmented inference, graph-augmented attention, and chain-of-thought verification.
Establish protocols for model lifecycle governance, safe update cycles, drift-aware re-training, and provenance tracking.
Train large-scale deep learning models, including multimodal architectures and domain-specific transformer-based systems, on real clinical datasets.
Fine-tune and adapt large language models (LLMs) for clinical reasoning, summarization, question answering, agentic behavior, and instruction-following tasks.
Build retrieval-augmented pipelines using embeddings, vector stores, graph traversal, and clinically grounded context construction.
Develop evaluation methods for reasoning quality, temporal prediction accuracy, multimodal synergy, ablation-based robustness, and counterfactual behavior.
Create reference-grounded training datasets, structured reasoning tasks, and multimodal benchmarks to evaluate model performance.
Conduct hands-on experimentation with optimization strategies, large-scale distributed training, model quantization, and inference acceleration.
Implement uncertainty modeling, selective prediction, abstention mechanisms, and clinically meaningful risk thresholds.
Build interpretable reasoning pathways, cross-modal attribution maps, and reference-grounded explanations.
Work closely with the Representation team to ensure representation-model alignment.
Partner with clinical SMEs to encode domain reasoning into reinforcement learning, preference optimization, or rule-guided behaviors.
Serve as the future founding technical lead of the Foundational Model Science Program.
Mentor scientists and engineers and eventually build a specialty modeling team.