Data Scientist Research Sovereign AI

Rochester, NH - USA

Monthly Salary: Not Disclosed

Posted on: 6 hours ago

Vacancies: 1 Vacancy

Job Summary

Description

Data ScientistFoundational 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 Mayos 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 playercoach guiding strategy and building a future team.

Key Responsibilities

Scientific & Technical Leadership

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.

Hands-On Modeling & Training

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.

Cross-functional Collaboration

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.

Team Leadership

Serve as the future founding technical lead of the Foundational Model Science Program.
Mentor scientists and engineers and eventually build a specialty modeling team.

Qualifications

PhD in Machine Learning Computer Science Applied Mathematics or related discipline with at least four years of informatics Artificial Intelligence data science and/or machine learning.
Experience with generative modeling reasoning models or multimodal foundation models.
Expertise in alignment methods (contrastive learning RLHF/RLCS preference optimization).
Experience with distributed training and large-scale compute.

Preferred

Experience with clinical or EMR data across multiple modalities.
7 years experience training deep learning models including transformers or multimodal architectures.
Experience defining evaluation frameworks for reasoning multimodal synergy reliability or fairness.
Publications in multimodal learning foundation models or reasoning architectures.

Required Experience:

DescriptionData ScientistFoundational Model SciencePosition SummaryThe Data Scientist for Foundational Model Science is the senior technical leader and the lead scientist responsible for designing training and governing Mayos multimodal foundational model. This model forms the core intelligence laye...

Description

Data ScientistFoundational Model Science

Position Summary

Key Responsibilities

Scientific & Technical Leadership

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.

Hands-On Modeling & Training

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.

Cross-functional Collaboration

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.

Team Leadership

Serve as the future founding technical lead of the Foundational Model Science Program.
Mentor scientists and engineers and eventually build a specialty modeling team.

Qualifications

PhD in Machine Learning Computer Science Applied Mathematics or related discipline with at least four years of informatics Artificial Intelligence data science and/or machine learning.
Experience with generative modeling reasoning models or multimodal foundation models.
Expertise in alignment methods (contrastive learning RLHF/RLCS preference optimization).
Experience with distributed training and large-scale compute.

Preferred

Experience with clinical or EMR data across multiple modalities.
7 years experience training deep learning models including transformers or multimodal architectures.
Experience defining evaluation frameworks for reasoning multimodal synergy reliability or fairness.
Publications in multimodal learning foundation models or reasoning architectures.

Required Experience:

Key Skills

Laboratory Experience
Machine Learning
Python
AI
Bioinformatics
C/C++
R
Biochemistry
Research Experience
Natural Language Processing
Deep Learning
Molecular Biology

Apply Now

About Company

Mayo Clinic

Why Mayo Clinic Mayo Clinic is top-ranked in more specialties than any other care provider according to U.S. News & World Report. As we work together to put the needs of the patient first, we are also dedicated to our employees, investing in competitive compensation and comprehensive ... View more

View Profile View Profile

AI AutoApply

Apply to 100+ jobs with one click