Healthcare Delivery AI Evaluation Architect

Sutter Health•Sacramento, CA

2d•Onsite

About The Position

Participates in artificial intelligence (AI) governance by developing adoption and evaluation methodology and scale AI-enabled workflows using evidence-based implementation methods. The role will co-design egoals and monitoring plans with business and clinical stakeholders, align success metrics to operational and clinical goals, and run hybrid effectiveness–implementation studies with rapid-cycle learning (PDSA). Responsibilities will include standing up reusable toolkits for readiness, fidelity tracking, equity monitoring, and value assessments, ensuring AI is usable, adopted, safe, effective and equitable. Collaborates on abstracts/manuscripts and practice-oriented publications to disseminate results internally and externally.

Requirements

Bachelor's in Applied Statistics, Computer Science or related field
5 years recent relevant experience
Expertise in study design methods (e.g., A/B, pre/post testing)
Expertise in statistical analyses (e.g., power/sample size; subgroup/equity analyses; cost and budget-impact/value realization)
Knowledge of SQL, Python or R
Knowledge of reproducible analytics
Skilled facilitator with executives and clinicians
Able to translate methods into decision-ready insights
Establish and maintain cooperative working relationships with clients, IS team members, management, and executive personnel/staff
Set priorities which accurately reflect the relative importance of job responsibilities
Prioritize assignments to complete work in a timely manner
Analyze information, problems, situations, practices, or procedures in order to identify relevant concerns and factors
Skilled in developing documentation at a technical and user level
Experience with publication-grade writing

Responsibilities

Develop adoption and evaluation methodology for AI governance.
Scale AI-enabled workflows using evidence-based implementation methods.
Co-design egoals and monitoring plans with business and clinical stakeholders.
Align success metrics to operational and clinical goals.
Run hybrid effectiveness–implementation studies with rapid-cycle learning (PDSA).
Stand up reusable toolkits for readiness, fidelity tracking, equity monitoring, and value assessments.
Ensure AI is usable, adopted, safe, effective and equitable.
Collaborate on abstracts/manuscripts and practice-oriented publications to disseminate results internally and externally.