Lead AI Governance Specialist

TIAA•Iselin, NJ

3d•$121,000 - $165,000•Onsite

About The Position

The Lead Data Quality and Governance Analyst manages large projects and processes relating to the assessment, improvement and governance of quality and ongoing fitness-for-purpose of data, ensuring adequate data quality is maintained so that data can effectively support business processes. As a subject matter expert in data quality and governance, this job directs the design and implementation of master data management solutions in order to maintain a common, firm-wide data standard. This job coaches, reviews and delegates work to lower level team members and influences the team to implement new processes and methodologies as needed.

Requirements

Governance framework design in regulated industries Minimum 5+ years designing or redesigning multi-stakeholder governance frameworks (e.g., risk tiering models, classification schemes, decision-rights matrices) within a regulated industry (financial services, insurance, healthcare, or similar). Candidate must present one specific example of a framework taken from design through pilot to operational SOP, including the system analysis or system architecture context in which it was applied.
Cross-functional stakeholder management Documented experience facilitating structured working sessions with 4 or more distinct control/compliance functions (e.g., any combination of Cyber, Risk, Model Risk Management, Legal, Ethics, Privacy) to align on a shared framework or policy. Candidate must describe at least one instance of conflicting requirements across functions and how resolution was reached.
Pilot execution with quantified outcomes Track record of piloting a process or framework change and reporting results against predefined metrics (e.g., cycle time reduction, effort reduction, risk coverage) prior to scaling. Candidate must cite specific before/after metrics from a prior pilot.
Working knowledge of AI/ML lifecycle and model risk terminology Able to define and correctly apply core model risk concepts (tiering, materiality thresholds, validation) and describe, at a conceptual level, how an AI/ML system progresses from development to deployment. No coding or model-building experience required.
SOP and requirements documentation Demonstrated ability to produce auditable procedural documentation (SOPs, decision-rights charts, escalation paths) and functional requirements documents used by a separate technical team to build or automate a workflow.

Nice To Haves

Formal certification or direct work experience in Model Risk Management (e.g., SR 11-7 or equivalent regulatory framework exposure).
Experience writing functional requirements later implemented in a workflow automation or GRC tool. Candidate must name the specific tool or platform used.
Experience partnering with technical implementation teams to translate SOPs into system logic or workflow automation, without personally architecting the system.

Responsibilities

Leads the implementation of tools and processes to maintain firm-wide data standards.
Strengthens the organization's data governance capabilities to ensure that high data quality exists throughout the complete lifecycle of the data and that data controls are implemented to support business objectives.
Ensures business process, system support and data quality governance for master data through data coordination and integration to ensure efficient processes and consistent data flows to business and stakeholders.
Oversees the development of reports and dashboards to identify key business metrics, trends and analytical needs, and optimizes dashboard reporting for operations and management to enable data driven decision making.
Monitors data quality metrics, executes data quality audits and advises on process improvements in relation to master data processes.
Evaluates master data management metrics and provides recommendations regarding opportunities for strengthening data integrity, quality and availability across the enterprise.
Reviews and contributes to reports for management to quantify and articulate the business impact of data quality issues.