VP, Data Strategy & Governance with AI Enablement

Broadridge

Newark (NJ)

Ibrido

USD 150.000 - 190.000

Tempo pieno

14 giorni+

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Bonus Eligible

Descrizione del lavoro

Broadridge is seeking a dynamic Vice President of Data Management to lead strategy, governance, and scalable data solutions across financial services processes. You will partner with senior stakeholders to strengthen data quality, drive operational efficiency, and advance AI governance and responsible GenAI capabilities.

The role oversees cross-functional programs, focuses on data standards, lineage, and reporting, and requires 2x weekly in-office presence.

Competenze

10+ years of experience in data management within a financial services environment.
Proven leadership experience managing cross-functional initiatives, complex programs, or enterprise-level data and process transformation efforts.
Strong expertise in data governance, data quality, reporting, workflow optimization, and operational risk management.
Hands-on experience with data and reporting tools, including SQL, Power BI, Cognos, Oracle, Altair Monarch, and Microsoft Access.
Strong Python programming experience with data mining, data analysis, and automation capabilities; pandas experience preferred.
Advanced knowledge of statistical analysis, quantitative modeling, and analytical tool development.
Experience supporting business-critical platforms and applications, preferably within financial services or other highly regulated environments.
Demonstrated experience applying AI and GenAI tools to business process improvement, documentation automation, reporting enhancement, process mining, and workflow efficiency.
Practical understanding of prompt engineering, model evaluation, human-in-the-loop review, and AI output quality assurance.

Mansioni

Lead data management initiatives across business, operations, and technology teams, with ownership of data quality, reporting, controls, workflow optimization, and operational scalability.
Partner with senior business and functional leaders to gather, challenge, and refine requirements; translate them into actionable roadmaps, detailed documentation, and measurable delivery outcomes.
Define and govern data standards, lineage, controls, and quality frameworks to support critical financial services products, transactions, and operational processes.
Drive the use of analytics, statistical methods, and financial modeling to identify trends, assess impacts, resolve discrepancies, and improve decision-making.
Design and implement reporting frameworks, dashboards, and real-time monitoring capabilities that proactively identify issues, trends, exceptions, and operational risk indicators.
Lead cross-functional programs with significant complexity, resource demands, regulatory considerations, and business impact.
Identify and execute process improvement opportunities that increase efficiency, reduce manual effort, strengthen controls, and optimize end-to-end processing.
Develop and oversee specialized analytical tools, data models, and reporting assets for strategic initiatives and ongoing operational use.
Use AI and GenAI capabilities to improve organizational workflows, including AI-assisted requirements gathering, automated documentation, intelligent reporting, workflow automation, and process mining.
Establish practical AI use cases for data quality automation, exception management, root-cause analysis, and operational insight generation within a controlled financial services environment.
Apply prompt engineering, model evaluation, and output validation techniques to ensure AI-generated content is accurate, traceable, secure, and fit for business use.
Partner with risk, compliance, legal, and technology teams to implement AI governance, model risk controls, data privacy protections, and responsible use standards.
Interpret internal and external business challenges, industry developments, and regulatory expectations; recommend actions and best practices to improve products, services, and processes.
Mitigate operational risk by proactively escalating emerging issues, monitoring key controls, and embedding stronger data and reporting discipline across the organization.