VP Data Science

Vice President, Data Science

Reporting to the Chief Technology Officer, the Vice President of Data Science is a senior technology leader responsible for defining and executing the organization’s data science and AI strategy. This leader transforms data into actionable insights, drives AI/ML innovation, and partners with operational leadership to influence product direction, operational excellence, and business outcomes. The VP of Data Science builds and leads high-performing teams, establishes scalable data science practices, and ensures models and insights are delivered ethically, reliably, and with measurable business impact.

• Define and own the enterprise data science vision, roadmap, and operating model aligned with company strategy.
• Translate business priorities into high-impact analytics, AI, and machine learning initiatives.
• Serve as an executive advisor on data-driven decision-making, AI adoption, and emerging technologies.
• Establish success metrics and ROI measurement for data science initiatives.

• Build, mentor, and scale a diverse, high-performing organization of data scientists and ML engineers.
• Set clear expectations, career paths, and performance standards.
• Foster a culture of curiosity, rigor, and collaboration.
• Create an environment where teams are empowered, accountable, and closely connected to the business.

• Oversee the design, development, deployment, and lifecycle management of predictive and prescriptive models.
• Ensure solutions are production-grade, integrated into applications and workflows, and supported by strong MLOps practices.
• Partner with engineering teams to embed models into clinical and operational systems.
• Establish governance, validation, and monitoring processes consistent with HIPAA and HITRUST requirements.

• Maintain a hands‑on approach to data science and AI, including the ability to write, review, and guide code in modern data science and machine learning stacks.
• Stay close to the work by participating in model design, experimentation, and technical decision‑making—especially for high‑impact or clinically sensitive use cases.
• Provide technical leadership and mentorship by reviewing approaches, validating assumptions, and ensuring analytical rigor and model quality.
• Partner with data scientists and ML practitioners to unblock complex problems and set technical direction, without micromanaging execution.
• Serve as a credible technical voice with Data Engineering and Application Development teams on architecture, model deployment, and MLOps practices.
• Balance hands‑on contribution with executive leadership, ensuring the organization benefits from both technical depth and strategic oversight.
• Lead by example in adopting best practices in machine learning, responsible AI, model explainability, and production readiness in a HIPAA‑regulated environment consistent with the HITRUST framework.

• Work closely with Data Engineering to ensure data quality, availability, and scalability.
• Collaborate with Application Development to embed analytics and models directly into workflows and products.
• Align on architecture, tooling, and MLOps practices that support both innovation and operational excellence.

• Data science solutions are embedded into daily clinical and operational workflows, not siloed.
• Operation leaders and clinicians trust and rely on ML/AI to guide decisions.
• Models and insights are delivered quickly, responsibly, and with clear ROI.
• Data science is seen as a strategic partner, not a support function.

• Bachelor’s degree in a quantitative field (Computer Science, Statistics, Mathematics, Engineering, or similar).
• 10+ years of experience in data science, analytics, machine learning, or applied AI, with 3+ years in senior leadership roles.
• Proven track record of delivering data science solutions with clear business impact at scale.
• Deep expertise in statistical modeling, machine learning, and experimental design.
• Experience operationalizing models in production environments.
• Demonstrated success…