Data & AI Technology Delivery Lead - Vice President

In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities. This is a Prin Technology Admin Office position at the Vice President Level, which is part of the job family responsible for managing administrative tasks related to technology infrastructure and services, ensuring smooth operations and support for the organization's technology needs. The Data & AI Technology Delivery Lead is a hybrid technology leadership role combining Fleet Enablement, Service Delivery Management, program execution, and Data & AI domain leadership. The role is responsible for translating the Data & Analytics strategy into coordinated execution across engineering teams, product owners, infrastructure partners, control functions, business stakeholders, and strategic vendors. The individual will provide centralized ownership of planning, prioritization, delivery governance, stakeholder communication, and operational readiness across the Data & AI platform portfolio. Unlike a traditional project-management role, this position requires sufficient technical depth to understand enterprise data platforms, AI capabilities, architecture dependencies, production-readiness requirements, and governance controls. The individual will act as the connective layer between technology strategy and execution, ensuring that priority capabilities move from evaluation through approval, onboarding, and production adoption. This role builds on the established Fleet Enablement model, which includes roadmap alignment, dependency management, blocker removal, fleet ceremonies, metrics, external-partner engagement, and continuous improvement What you’ll do in the role: 1. Fleet Enablement and Strategic Execution Partner with Fleet and technology leadership to translate strategic priorities into structured roadmaps, bodies of work, milestones, and measurable outcomes. Coordinate planning and execution across Data & Analytics squads, product teams, operations, architecture, cybersecurity, legal, procurement, and control functions. Maintain an integrated view of priorities, dependencies, capacity constraints, delivery risks, and critical decisions. Facilitate fleet-level governance and operating forums, including roadmap reviews, leadership updates, planning sessions, and cross-product working groups. Identify and remove organizational, technical, or process blockers that affect delivery. Promote consistent execution practices and continuous improvement across the fleet. Ensure squads remain aligned to approved priorities, roadmaps, and key performance indicators. These responsibilities align with the Fleet Enablement framework’s expectations for managing dependencies, removing blockers, facilitating ceremonies, tracking metrics, supporting roadmap delivery, and collaborating with external partners 2. Service Delivery Management Serve as a central point of accountability for the delivery and ongoing operation of Data & AI platform capabilities. Establish and maintain transparent intake, prioritization, planning, and delivery-tracking processes. Manage and prioritize work across platform hygiene, core functionality, service improvements, control remediation, client onboarding, and strategic AI enablement. Track commitments, milestones, risks, incidents, dependencies, remediation actions, and production-readiness requirements. Coordinate delivery across engineering teams, product owners, infrastructure partners, vendors, and internal clients. Provide clear escalation of delivery risks, resource constraints, service issues, and decisions requiring leadership attention. Support service governance, lifecycle management, capacity planning, operational readiness, resilience, and control obligations. Balance strategic change with operational stability, regulatory requirements, and platform sustainability. 3. Data & AI Technology Leadership Provide technology leadership across enterprise data, analytics, and AI platforms, including cloud data platforms, BI and analytics, data integration, and AI-enabled engineering solutions. Develop a working understanding of platform architectures, capabilities, limitations, governance requirements, and production-readiness dependencies. Translate business and technology requirements into actionable delivery plans for engineering and operational teams. Coordinate the assessment and enablement of emerging platform capabilities from initial evaluation through architecture, control review, production readiness, adoption, and ongoing service management. Support initiatives involving platforms such as Snowflake, Databricks, Dataiku, Power BI, MongoDB, and related enterprise Data & AI services. Partner with engineering and architecture leads to identify cross-platform dependencies, technical risks, and required design decisions. Help teams choose the appropriate platform or capability based on business requirements, control obligations, cost, scalability, and operational supportability. Maintain consolidated views of platform status, approved capabilities, emerging features, use cases, dependencies, and adoption paths. The current Data & AI portfolio spans governed enterprise data platforms, semantic foundations, foundation models, agent orchestration, AI operations, observability, and AI economics. 4. Governance, Risk, and Production Readiness Coordinate security architecture, legal, compliance, data-governance, and operational-readiness activities for new platform capabilities and integrations. Ensure required approvals, control evidence, architecture artifacts, action plans, and remediation items are clearly owned and tracked. Manage dependencies across policy, access controls, data classification, audit logging, vendor integrations, cost controls, and service monitoring. Partner with control functions and engineering teams to move capabilities through evaluation, build approval, production approval, and controlled adoption. Identify gaps early and establish clear remediation plans, owners, milestones, and escalation paths. Ensure that AI enablement is delivered within approved enterprise governance and risk frameworks. Existing Data & AI work includes centralized intake and governance, cross-product control accountability, security-architecture alignment, AI workload orchestration, and coordination with policy and control stakeholders. 5. Stakeholder and Executive Engagement Act as a trusted interface among senior leadership, engineering teams, product owners, control partners, business stakeholders, and vendors. Prepare concise, decision-oriented reporting covering progress, risks, blockers, dependencies, required decisions, and next steps. Translate complex technical topics into clear business impact, adoption considerations, and leadership decisions. Coordinate executive materials, platform updates, governance submissions, meeting briefs, technology landscapes, and strategic presentations. Ensure stakeholder expectations are realistic and aligned with technical capacity, controls, and delivery dependencies. Maintain effective communication across technical and nontechnical audiences. The role’s current operating context includes preparing consolidated platform updates, capability summaries, technology landscapes, leadership materials, newsletters, meeting notes, and vendor briefings. 6. Vendor and Partner Management Coordinate engagement with strategic technology vendors and internal partner organizations. Organize technical deep dives, roadmap reviews, demonstrations, issue-resolution sessions, training, and executive engagements. Track vendor commitments, product gaps, feature requests, delivery dependencies, and follow-up actions. Consolidate feedback from engineering, operations, architecture, control partners, and users into actionable vendor discussions. Assess vendor capabilities in the context of enterprise architecture, operational requirements, governance, cost, and business value. Support broader adoption through practical guidance, learning sessions, documentation, and enablement activities. The Data & Analytics portfolio relies on recurring vendor engagement, enablement sessions, roadmap discussions, technical workshops, and use-case support. 7. Metrics, Reporting, and Continuous Improvement Define and maintain measurable indicators for delivery progress, adoption, operational health, control closure, and business value. Develop executive-ready dashboards and reporting that provide a consolidated view across platforms and initiatives. Improve the consistency, quality, and traceability of status reporting and source data. Track decisions, approvals, action items, dependencies, and accountable owners. Use platform and delivery data to identify bottlenecks, recurring issues, and improvement opportunities. Promote reusable processes, templates, documentation, and knowledge-sharing practices across the fleet. What you’ll bring to the role: Experience in technology delivery, service delivery management, technical program management, product enablement, or a comparable leadership role. Demonstrated experience coordinating complex, cross-functional initiatives involving engineering, operations, architecture, cybersecurity, risk, and business stakeholders. Working knowledge of enterprise data and analytics technologies, including one or more of the following: Cloud data platforms Data warehouses and lakehouse platforms Data integration and ETL/ELT Business intelligence and analytics Machine learning or generative AI platforms Enterprise database and data-infrastructure services Ability to understand technical architectures, dependencies, control requirements, and production-readiness considerations. Strong delivery discipline, including roadmap management, prioritization, dependency management, risk tracking, and escalation. Strong written and verbal communication skills, with the ability to communicate effectively with engineers, control partners, vendors, and senior executives. Experience producing concise leadership updates, decision materials, technical summaries, and delivery reporting. Demonstrated ability to operate across multiple initiatives while maintaining attention to detail, accuracy, and follow-through. Proficiency with enterprise workflow and collaboration tools such as Jira, Confluence, Microsoft 365, and reporting or presentation tools. Preferred Qualifications Experience with platforms such as Snowflake, Databricks, Dataiku, Power BI, MongoDB, Azure, or AWS. Experience supporting AI or advanced-analytics capabilities through architecture review, governance, production readiness, and adoption. Familiarity with enterprise security architecture, technology-risk processes, data governance, and regulatory control environments. Experience with Agile, Scrum, Kanban, Fleet, or product-based operating models. Experience facilitating senior-level governance, steering, and decision forums. Experience managing strategic technology-vendor relationships. Relevant certifications in Agile, project management, cloud, data, or AI technologies are beneficial but not required. Core Competencies Technical credibility: Understands Data & AI concepts sufficiently to challenge assumptions, identify dependencies, and facilitate informed decisions. Execution leadership: Converts broad objectives into structured delivery plans, owners, milestones, and measurable outcomes. Stakeholder management: Builds alignment across teams with different priorities, responsibilities, and levels of technical expertise. Risk awareness: Identifies delivery, operational, governance, and control risks before they affect commitments. Executive communication: Presents complex information in a concise, decision-oriented format. Problem solving: Navigates ambiguity and connects the appropriate stakeholders to resolve blockers. Operational discipline: Maintains accurate plans, status, actions, documentation, and governance records. Adaptability: Operates effectively across strategic planning, technical delivery, governance, and urgent execution needs. Collaboration: Builds productive relationships across internal teams, business partners, and vendors. Ownership: Drives work through completion and ensures that commitments do not become fragmented across organizational boundaries. The Data & Analytics portfolio spans multiple shared platforms, engineering teams, control functions, business stakeholders, and strategic vendors. It must simultaneously deliver platform stability, lifecycle and control obligations, client onboarding, modernization, and new AI capabilities. Without dedicated ownership of service delivery and fleet enablement, accountability becomes fragmented, cross-team dependencies may surface too late, and engineering leaders must absorb coordination and reporting responsibilities that reduce their capacity to deliver technical outcomes. The role provides the centralized execution leadership needed to protect operational stability while accelerating the delivery and adoption of strategic Data & AI capabilities. WHAT YOU CAN EXPECT FROM MORGAN STANLEY: At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work. To learn more about our offices across the globe, please copy and paste into your browser. Expected base pay rates for the role will be between $155,000 and $215,000 per year at the commencement of employment. However, base pay if hired will be determined on an individualized basis and is only part of the total compensation package, which, depending on the position, may also include commission earnings, incentive compensation, discretionary bonuses, other short and long-term incentive packages, and other Morgan Stanley sponsored benefit programs. Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents. Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences. For more information, please visit : .