Sr Healthcare AI Business Analyst

Cayuse Holdings•Atlanta, GA

5d•$75 - $95•Hybrid

About The Position

We are seeking an experienced IT Business Analyst to drive the comprehensive requirements gathering, discovery phase, and baseline prioritization framework for our enterprise-wide Artificial Intelligence Transformation Strategy. The selected candidate will act as the principal architectural bridge between Client’s clinical, administrative, and financial operational executives and advanced data science engineering teams. The absolute core objective of this mandate is executing deep executive discovery and structured technical workshops to synthesize unstructured organizational pain points into an actionable, multi-year AI investment roadmap.

Requirements

5+ years of progressive IT Business Analysis experience, with a heavy preference for individuals who have executed enterprise strategy mandates within a matrixed multi-hospital healthcare system.
Proven understanding of data science principles, machine learning lifecycles, structured vs. unstructured data ingestion, natural language processing (NLP), Large Language Models (LLMs), and technical constraints inherent to healthcare AI safety.
Demonstrated mastery in leading highly structured discovery interviews and multi-stakeholder workshops with C-Suite executives, clinical chiefs, and administrative directors.
Must be available to conduct intensive, face-to-face workshop facilitation and on-site discovery sessions directly inside Client facilities located across the greater Atlanta area as required.
Advanced proficiency in Agile frameworks, product backlog management, workflow visualization tools (Visio/Lucidchart/Miro), and structured data assessment techniques (SQL, data dictionary profiling).

Nice To Haves

5+ years of progressive IT Business Analysis experience, with experience executing enterprise strategy mandates within an Academic Medical Center.

Responsibilities

Plan, architect, and conduct structured interviews and collaborative workshops with senior leaders and domain subject-matter experts (SMEs).
Map Enterprise KPI Ecosystems: Understand discrete organizational goals, key performance indicators (KPIs), and targeted measures of success native to each leadership vertical.
Isolate Operational Blockers: Explicitly identify operational, clinical, financial, and administrative challenges impeding the execution of core strategic objectives.
Audit Optimization Target Baselines: Document existing transformation targets, including current cost reduction initiatives, workforce optimization mandates, productivity metrics, and revenue enhancement targets.
Inventory Leverageable Assets: Audit and document active resources capable of being deployed for immediate AI optimization, including unallocated financial capital, human resources, data pipelines, and infrastructure capabilities.
Proactively Introduce Proven Benchmarks: Inject and present contextualized, high-viability AI investment opportunities derived from successful implementations at peer Academic Medical Centers and leading health systems.
Capture Qualitative Visions: Formalize leadership's specific perspectives on where AI models (generative, predictive, analytical) can maximally optimize clinical outcomes, operational efficiency, and end-user experiences.
Build Discovery Governance Pipelines: Pinpoint key downstream stakeholders, workflow/process owners, and data SMEs critical for subsequent detailed discovery work and robust business case modeling.
Evaluate Organizational AI Readiness: Assess operational departments for AI adoption readiness, explicitly identifying executive champions, anticipated pockets of cultural friction, and change-management speed factors.
Structure and populate an explicit prioritization taxonomy divided into three distinct operational tiers: Tier 1: AI Quick Wins, Tier 2: Deep Discovery Opportunities, Tier 3: Capital Planning Pipeline.