VP, Data Science and AI

Accuity

1d•Remote

About The Position

To continue to increase the reach of services and drive business growth through the application of modern data science and applied AI methods, Accuity is seeking a VP, Data Science & AI to lead a team of skilled resources in support of the strategic priority to become a data-driven and outcomes-oriented organization. The successful candidate will bring deep experience with machine learning, predictive modeling, statistical analysis, mathematical optimization, algorithm development, artificial intelligence, and a passion for working with healthcare data. This role leads Accuity’s internal data science team and serves as the technical owner and counterpart for external AI development partners, accountable for the methodology, evaluation rigor, and production performance of AI systems including generative and agentic LLM-based systems that Accuity deploys and must operate independently over time. Reporting to the Chief Technology Officer, this role is responsible for advancing Accuity’s data science and AI roadmap, governing predictive and generative/LLM-based systems, and ensuring AI solutions are measurable, auditable, scalable, compliant, and operationally effective in a PHI/HIPAA-regulated healthcare environment.

Requirements

Bachelor’s degree in Computer Science, Information Technology, Business Administration, Applied Mathematics, Statistics, Data Science, Engineering, or a related discipline required.
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.
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.

Nice To Haves

Master’s degree in a related field preferred.

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.
Monitor, manage, and optimize production performance for predictive and generative/LLM-based systems, including subgroup performance, quality trends, calibration, drift, and measurable business impact.
Lead rapid testing, deployment, validation, and iteration cycles to improve model and system performance over time.
Partner with data architecture, engineering, security, and operations teams to operationalize new models and AI systems into stable, scalable production workflows.
Maintain hands-on technical credibility through direct engagement in data analysis, model evaluation, solution design, technical review, and applied AI problem solving as needed.
Serve as Accuity’s technical owner and counterpart for external AI development partners, including partners supporting generative AI, LLM-based, and agentic systems.
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.
Lead knowledge transfer from external partners to Accuity teams so delivered systems can be operated, maintained, evaluated, and improved without ongoing vendor dependency.
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 AI governance for clinical decision systems, including model risk, human-in-the-loop requirements, decision traceability and auditability, calibration, and PHI-safe model routing.
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.
Hire, lead, develop a high-performing, results-oriented internal data science team.
Foster a growth-oriented, inclusive, accountable, and high-performance team culture.
Set clear priorities, expectations, performance measures, and development plans for team members.
Build team capabilities in applied AI, evaluation-driven development, ML and LLM operations, healthcare analytics, and production performance management.
Maintain effective communication, team alignment, and collaboration in Accuity’s fully remote work environment.
Coordinate data science and AI initiatives across Technology, Product, Operations, Finance, Clinical, Compliance, Security, and executive leadership.
Communicate program performance, risks, tradeoffs, and business impact clearly to technical and non-technical audiences, including C-suite and executive stakeholders.
Partner with business operators and functional leaders to ensure AI solutions are designed for practical workflow adoption, measurable value, and long-term operational ownership.
Serve as a trusted technical advisor on AI methodology, vendor deliverables, production readiness, and responsible deployment.
Perform other duties and support additional initiatives assigned by leadership.