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

Insulet Corporation•Acton, MA

2d•$225,000 - $337,500•Hybrid

About The Position

The Senior Director, Applied AI will define and lead Insulet’s enterprise AI vision, strategy, and operating model, translating critical business priorities into scalable AI and data-driven solutions that deliver measurable outcomes. As a senior leader within the Enterprise AI and Data organization, this role owns a multi-year portfolio across patient engagement, customer experience, commercial and growth effectiveness, market access, digital, operations, and enterprise productivity. The leader will shape AI investment choices, establish adoption and outcome standards, and move applied AI capabilities from experimentation to sustained business impact. The role oversees the end-to-end portfolio of applied AI products and capabilities, from discovery and prioritization through production deployment, adoption, and lifecycle management. It leads a multi-layer organization of directors, senior managers, product leaders, data scientists, AI engineers, analytics professionals, and cross-functional delivery teams, while influencing the CTO, ELT, and senior business leaders. The position also provides executive leadership for responsible AI deployment, partnering with relevant control and technology functions to manage privacy, compliance, quality, regulatory, and enterprise risks. This position is eligible for hybrid working arrangements and requires on-site work from an Insulet office. The stated work location is Acton, Massachusetts. The U.S.-based annual base salary range is $225,000 to $337,500, and the role may also be eligible for incentive compensation. Actual pay depends on skills, experience, and education. The posting remains open until the position is filled.

Requirements

Master’s degree in computer science, engineering, data science, analytics, business, or a related field.
18+ years of progressive experience leading enterprise-scale data, analytics, AI, digital, or technology organizations, including significant leadership of leaders and multidisciplinary teams.
Demonstrated experience setting multi-year enterprise strategy, owning complex portfolios and investment decisions, and delivering measurable business outcomes across multiple functions.
Strong understanding of applied AI, machine learning, generative AI, large language models, AI agents, RAG, copilots, predictive models, and workflow automation.
Demonstrated ability to influence executive stakeholders and communicate strategy, investments, value, and risk at CTO and ELT levels.

Nice To Haves

Experience in healthcare, medical devices, life sciences, diabetes care, digital health, or other regulated industries.
Experience scaling AI agents, customer service copilots, commercial recommendation engines, patient engagement intelligence, or other enterprise AI products.
Strong understanding of data governance, MDM, customer, provider, patient, and commercial data, and enterprise analytics.
Familiarity with Salesforce, ServiceNow, Genesys, AWS, Azure, Databricks, Snowflake, or similar enterprise technology ecosystems.

Responsibilities

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 AI 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 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.
Own the end-to-end portfolio of applied AI products and capabilities across 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 AI-enabled products and business capabilities.
Scale reusable capabilities such as 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.
Provide executive leadership for responsible applied AI deployment in accordance with 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 risk and define scalable controls.
Ensure human oversight, auditability, output validation, content safety, escalation, and accountability are embedded in customer- and patient-facing AI solutions.
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.
Influence enterprise business and technology strategy through partnerships with the CTO, ELT, and senior leaders across Commercial, Growth, Customer Experience, Digital, Operations, R&D, Quality, Regulatory, Finance, and Technology.
Lead enterprise change and adoption strategies that embed AI into workflows, leadership routines, capability building, and decision-making at scale.