VP, Data Science and AI

Responsibilities

• Serve as the company-wide leader for data science and applied AI initiatives, including predictive modeling, generative AI, LLM-based systems, and agentic workflows.
• Build and maintain Accuity’s data science and AI roadmap in alignment with enterprise technology priorities, business growth goals, operational needs, and client value.
• Identify opportunities across the business to apply data science and AI to improve clinical documentation integrity, coding accuracy, reimbursement optimization, quality outcomes, productivity, and decision support.
• Develop business cases, success criteria, and return-on-investment measures for data science and AI initiatives.
• Translate business, clinical, operational, and financial needs into clear technical requirements, solution approaches, implementation plans, and measurable outcomes.
• Own evaluation-driven development, including ground-truth datasets, evaluation frameworks, and the metrics that determine whether AI systems are production-ready.
• Define and compute the performance metrics that govern commercial and incentive terms, ensuring they are reproducible and auditable.
• Maintain hands-on technical credibility through direct engagement in data analysis, model evaluation, solution design, technical review, and applied AI problem solving as needed.
• Partner with data architecture, engineering, security, and operations teams to operationalize new models and AI systems into stable, scalable production workflows.
• Lead rapid testing, deployment, validation, and iteration cycles to improve model and system performance over time.
• Collaborate with external AI partners, vendors, prospective clients, current clients, and the PE-partner Center of Excellence as needed to support strategic technology initiatives and active engagements.
• Lead knowledge transfer from external partners to Accuity teams so delivered systems can be operated, maintained, evaluated, and improved without ongoing vendor dependency.
• Provide technical oversight of external AI partners by validating their architecture, methodology, deliverables, and performance against defined milestones and production-readiness gates.
• Hold external partners accountable for evaluation rigor, documentation, system performance, reproducibility, auditability, and contractual commitments.
• Ensure AI solutions are developed and operated in accordance with HIPAA, BAA, information security, privacy, compliance, and risk management requirements.
• Establish governance processes for responsible AI use, including documentation standards, evaluation records, model/system performance monitoring, and escalation criteria.
• Partner with legal, compliance, security, technology, clinical, and operational stakeholders to ensure AI systems are appropriate for use in regulated healthcare workflows.
• Support AI implementation practices using secure and compliant platforms, including Azure AI Foundry, Azure ML, and related enterprise AI infrastructure.

Qualifications

• Bachelor’s degree in Computer Science, Information Technology, Business Administration, Applied Mathematics, Statistics, Data Science, Engineering, or a related discipline required.
• Master’s degree in a related field preferred.
• 8+ years of hands-on experience in data science, machine learning, or applied AI, including technical team leadership.
• Production experience with predictive and generative AI / LLM-based systems.
• Experience with AI/ML evaluation, monitoring, and governance.
• Experience overseeing external AI/ML vendors.
• Proficiency with Python, SQL, Azure AI/ML platforms, and related tools.
• Healthcare analytics experience required; PHI/HIPAA-regulated environment experience strongly preferred.

Core Competencies

• Applied AI Leadership: Demonstrates strong judgment in selecting, evaluating, governing, and operationalizing predictive, generative, and LLM-based AI systems.
• Evaluation Rigor: Uses measurable, reproducible, and auditable methods to assess model/system quality, readiness, risk, and business impact.
• Strategic Thinking: Connects data science and AI capabilities to enterprise priorities, client outcomes, operational performance, and long-term scalability.
• Technical Credibility: Engages deeply enough with architecture, methodology, metrics, and implementation details to guide internal teams and challenge external partners effectively.
• Governance and Risk Orientation: Balances innovation with responsible AI practices, clinical decision support considerations, PHI/HIPAA requirements, auditability, and model risk management.
• Executive Communication: Communicates complex technical concepts, performance results, risks, and recommendations clearly to executive, operational, technical, and non-technical audiences.
• Stakeholder Management: Builds trust and alignment across Technology, Operations, Finance, Clinical, Compliance, Security, clients, vendors, and external partners.
• People Leadership: Develops talent, sets clear expectations, provides coaching, drives accountability, and builds an inclusive, high-performing remote team.
• Execution Discipline: Moves complex AI initiatives from concept to production through clear priorities, milestones, ownership, performance gates, and measurable outcomes.
• Problem Solving: Uses analytical thinking, experimentation, and sound judgment to resolve ambiguous, complex, and cross-functional business and technology challenges.
• Remote Work Effectiveness: Communicates proactively, documents decisions clearly, manages distributed collaboration effectively, and maintains alignment across a fully remote organization.