Transformation Program Management Lead, Markets -VP

This role is a senior analytical professional within Markets Transformation & Program Execution. The individual will apply data analysis, data engineering, quantitative methods, and emerging technologies to support transformation programs, inform business decisions, and accelerate the translation of regulatory and business requirements into practical solutions.   Working with product owners, risk specialists, business subject matter experts, technology teams, and program leaders, the individual will analyze complex data, evaluate data quality, prototype decision logic, and design sustainable data solutions. The role requires the ability to translate business and regulatory concepts into structured analytical approaches and communicate effectively with both business and technical stakeholders.   This role's primary focus is applied analysis, data engineering, quantitative problem-solving, rapid prototyping, and the design of agentic workflows. The successful candidate will independently structure ambiguous problems, apply a disciplined and evidence-based approach, and deliver high-quality analytical outputs that support Markets priorities.   The role will work with sensitive, confidential, and controlled business data. The individual must apply appropriate data-handling, access-control, documentation, and governance practices throughout the analytical lifecycle.

Support transformation programs and project decision-making by answering complex business questions through data. Partner with product owners, risk specialists, business experts, technology teams, and program stakeholders to define analytical questions, identify data requirements, and interpret results. Analyze complex data models using SQL and Python to answer business questions, assess data quality, identify relationships and anomalies, and evaluate potential solutions. Develop reusable frameworks and programmatic solutions for recurring and ad hoc data problems, including cross-system regression testing, data-quality validation, data-model analysis, and data-pipeline assessment. Rapidly develop proofs of concept and prototypes based on regulatory rule text, business interpretations, or emerging program requirements to clarify requirements, test assumptions, reduce uncertainty, and inform the development of longer-term data models, technology platforms, and production systems. Translate business and regulatory requirements into testable decision logic, algorithms, decision trees, data models, and prototype analytical solutions. Decompose complex business processes and real-world concepts into appropriate data structures, including normalized models, entities, attributes, relationships, and reference data. Contribute to the design and assessment of data pipelines, transformation logic, workflow dependencies, and enterprise-scale data environments. Identify activities for which agentic large language model solutions may be beneficial and design appropriate workflows to improve efficiency, automate suitable tasks, and reduce time to market for analytical results. Apply relevant quantitative financial analysis techniques, which may include backtesting, risk modeling, derivative pricing, Monte Carlo analysis, and scenario testing. Develop visualizations, diagrams, documentation, and knowledge-management artifacts that communicate complex findings clearly and remain maintainable over time. Communicate analytical approaches, assumptions, data limitations, risks, and results to business and technical stakeholders. Manage assigned work independently using tools such as Jira, Confluence, and GitHub to organize activities, document decisions, maintain analytical artifacts, and support collaboration. Handle sensitive, confidential, and controlled data in accordance with applicable access controls, data-governance requirements, and information-security standards. Appropriately assess risk when making business decisions, with particular consideration for