EAM – Tech Strategy Metrics & KPI Lead; Hyrbid

We’re seeking a Strategy Metrics & KPI Lead (Vice President) to shape how Morgan Stanley measures progress and outcomes across the firm’s technology strategy. As part of the Enterprise Architecture and Modernization (EAM) Metrics & KPIs function, this role leads the firm’s technology measurement ecosystem, partnering across architecture, engineering, cybersecurity, data, infrastructure, and senior leadership to define trusted, standardized metrics that inform decision‑making across Technology Strategy priorities.

The role combines technology strategy, data architecture, analytics, KPI governance, and hands‑on implementation, leveraging AI technologies where appropriate. It will measure outcomes across modernization, governance, resilience, engineering excellence, platform adoption, data readiness, productivity, operational efficiency, and other strategic priorities. Measures may include AI adoption and productivity as part of a much broader technology strategy measurement mandate.

The successful candidate will establish the measurement foundations that help leaders assess outcomes, quantify value, and identify areas requiring action. This includes identifying key datasets, improving data quality, defining common metrics, and enabling broad consumption of strategy insights through executive dashboards, self‑service analytics, and reporting platforms.

The EAM team drives firmwide technology strategy through scalable, resilient architectures, governance, and standards. By advancing cloud, data, distributed systems, and AI capabilities, they accelerate modernization, optimize technology investments, and foster a global architecture community focused on innovation and long‑term value.

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 Lead Data & Analytics Engineering position at Vice President level, which is part of the job family responsible for providing specialist data analysis and expertise that drive decision‑making and business insights as well as crafting data pipelines, implementing data models, and optimizing data processes for improved data accuracy and accessibility, including applying machine learning and AI‑based techniques.

Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world.

• Partner with stakeholders across every division to define, standardize, and govern the metrics and KPIs tracking progress against the firm’s strategic technology priorities, building on the ongoing refresh of the technology strategy scorecard now underway across engineering, data, architecture, infrastructure, cybersecurity, and business teams.

• Develop leading and lagging indicators that give leadership visibility into adoption, enablement, modernization, simplification, technology health, resilience, governance maturity, productivity gains, operational efficiency, platform convergence, and measurable business value.

• Establish consistent measurement approaches across the major pillars of the technology strategy, including business and corporate enablement, technology modernization and simplification, governance and operating model maturity, resilience and risk reduction, engineering excellence, platform adoption, data readiness, and selective AI‑related measures where they contribute to broader strategy outcomes.

• Produce strategic insights, trend analyses, dashboards, and executive reporting that help leaders evaluate transformation impact and guide future investment, continuing the buildout of self‑service analytics environments maturing from static reporting into interactive, model‑driven exploration.

• Design and deliver the technical architecture that aggregates, publishes, and enables querying of enterprise strategy datasets, including reconciliation, normalization, stewardship, lifecycle management, and repeatable publication of underlying metrics.

• Define semantic models, metadata standards, business glossaries, lineage requirements, and data quality controls so that technology strategy metrics are consistently understood, discoverable, trusted, and reusable across the firm.

• Partner with data, engineering, architecture, cybersecurity, infrastructure