Senior Director, Applied AI - Hybrid (Acton, MA)

Job Summary

Senior Director, Applied AI

The Senior Director, Applied AI will define and lead Insulet’s enterprise AI vision, strategy, and operating model, transforming critical business priorities into scalable AI and data-driven solutions that deliver measurable outcomes.

This leader will oversee a diverse portfolio spanning patient engagement, customer experience, commercial effectiveness, market access, digital, operations, and enterprise productivity, while driving AI adoption and business value at scale.

The ideal candidate is a strategic enterprise leader who can influence at the executive level, shape investment priorities, and lead high-performing teams across product, data science, AI engineering, analytics, and cross-functional delivery to accelerate innovation and impact across the organization.



Senior Director, Applied AI

The Senior Director, Applied AI will set the enterprise vision, strategy, and operating model for the applied AI portfolio, translating Insulet's most critical business priorities into scalable AI and data products across multiple business functions.

As a senior leader within the Enterprise AI and Data organization, this role will own a multi-year portfolio spanning patient engagement, customer experience, commercial and growth effectiveness, market access, digital, operations, and enterprise productivity. The role will shape enterprise AI investment choices, establish outcome and adoption standards, and ensure applied AI capabilities move from experimentation to sustained business impact.

The ideal candidate is an enterprise leader who can operate at CTO and ELT levels while leading a multi-layer, diverse organization of directors, senior managers, product leaders, data scientists, AI engineers, analytics professionals, and cross-functional delivery teams.

Key Responsibilities

1. Enterprise Applied AI Strategy and Portfolio Ownership

• Define and own a 5+ year enterprise applied AI vision, strategy, capability roadmap, and investment portfolio aligned with company strategy and technology modernization priorities.

• Set enterprise priorities across patient engagement, customer service, sales operations, marketing effectiveness, provider engagement, market access, digital, operations, and enterprise productivity.

• Translate ambiguous enterprise challenges into strategic AI opportunities, investment theses, product portfolios, experiments, MVPs, and scaled capabilities.

• Present applied AI strategy, portfolio choices, investment recommendations, value realization, and risk assessments to the CTO, ELT, and senior business leaders.

• Partner with Finance and business executives to establish portfolio-level value cases and outcome measures, including growth, retention, productivity, cost, service quality, and patient outcomes.

2. Enterprise AI Product and Business Capability Leadership

• Own the end-to-end portfolio of applied AI products and capabilities across multiple business functions, from discovery and prioritization through production deployment, adoption, and lifecycle management.

• Establish product operating mechanisms, decision rights, roadmaps, funding priorities, and governance across a portfolio of AI-enabled products and business capabilities.

• Scale reusable applied AI capabilities, including patient adherence intelligence, next-best-action recommendations, segmentation, field effectiveness, marketing optimization, forecasting, customer service copilots, AI agents, and workflow automation.

• Drive enterprise integration of AI into CRM, service platforms, field tools, dashboards, digital experiences, and business operating rhythms.

• Hold leaders accountable for delivery discipline, adoption, measurable outcomes, risk management, and continuous improvement across the portfolio.

3. Responsible AI, Governance, and Enterprise Risk Leadership

• Provide executive leadership for responsible deployment of applied AI in accordance with Insulet's privacy, compliance, quality, and medical device industry expectations.

• Establish portfolio governance and decision frameworks for AI use cases involving patient, provider, customer, employee, and commercial data.

• Partner with Security, Privacy, Legal, Compliance, Quality, Regulatory, Data Governance, and Enterprise Architecture to assess enterprise risk and define scalable controls.

• Ensure appropriate human oversight, auditability, output validation, content safety, escalation, and accountability are embedded across customer- and patient-facing AI solutions.

4. Organizational Leadership and Enterprise Influence

• Build, lead, and develop a multi-layer, diverse organization of directors, senior managers, product leaders, data scientists, AI engineers, analytics professionals, and cross-functional delivery teams.

• Define the applied AI operating model, organizational capabilities, talent strategy, workforce plan, and leadership structure required to scale the portfolio.

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