As AI use cases rapidly expand across wealth management, the need for consistent, high-quality execution of model risk governance becomes critical.
This role sits at the center of execution-supporting the AI Risk Lead in operationalizing model risk frameworks, ensuring every AI use case is properly assessed, documented, tracked, and remediated in alignment with enterprise Model Risk Management (MRM) standards.
You will gain deep exposure to GenAI, machine learning, and agentic systems, while building expertise in how AI is governed within a regulated financial services environment.
What will you do?
1. Execute Model Risk Lifecycle Activities
Support the end-to-end model risk lifecycle, including:
- Intake and triage of AI use cases
- Model classification and tiering
- Coordination of governance stage gates (POC → pilot → validation readiness)
- Ensuring all required artifacts and documentation are complete prior to progression
2. Perform Model Risk Assessments
Conduct structured model risk assessments, including:
- Model purpose and usage validation
- Data inputs, lineage, and sensitivity analysis
- Model complexity and explainability considerations
- Identification of key risks (e.g., bias, overfitting, hallucination, misuse)
- Documentation of findings in standardized templates for review by senior risk stakeholders
3. Support Model Documentation & Validation Readiness
Prepare and maintain model documentation packages, including:
- Model design summaries
- Data sources and limitations
- Performance metrics and testing evidence
- Ensuring models meet enterprise MRM documentation standards prior to submission
- Coordinating with AI Engineering and Data Science teams to address documentation gaps
4. Track & Drive Model Findings Remediation
- Maintain a centralized model findings register, including validation findings, audit issues, and control gaps
- Track remediation progress across stakeholders (Engineering, Data, Product)
- Support evidence collection and packaging for formal closure with risk and audit teams
- Escalate risks or delays as appropriate
5. Monitor Model Inventory & Governance Status
- Maintain an up-to-date model inventory, including classification, tiering, and lifecycle stage (POC, pilot, validation, production)
- Track validation status and dependencies
- Support development of model risk dashboards and reporting
6. Support GenAI & Agent Risk Governance
Assist in evaluating risks related to:
- Prompt design and testing
- Retrieval-Augmented Generation (RAG) data sources and grounding quality
- Agent workflows and decision boundaries
- Documentation of control approaches and limitations specific to GenAI systems
7. Enable Risk Processes & Tooling
- Support development and maintenance of risk assessment templates
- Assist with intake and triage workflows
- Contribute to findings tracking tools and reporting dashboards
- Identify opportunities to streamline and scale governance processes
8. Cross-Functional Coordination
Collaborate closely with:
- AI Engineering and Data Science teams (model details and remediation)
- Data teams (lineage and data quality inputs)
- Enterprise Model Risk teams (validation coordination)
- Ensure efficient flow of information across stakeholders
What do you need to succeed?
Must-have
- Foundational understanding of:
- Data science and machine learning concepts
- Model development lifecycle (training, validation, deployment)
- Strong analytical and structured thinking skills
- Ability to interpret and document technical concepts within a risk and governance framework
- Experience in risk, governance, audit, or data-related roles
- High attention to detail with the ability to manage multiple workstreams simultaneously
Nice to have
- Exposure to Model Risk Management (MRM) frameworks
- Familiarity with GenAI / LLM concepts and associated risks
- Experience with:
- Data lineage or data quality tools
- SQL, Python, or data analysis tools
- Experience in financial services or other regulated environments
What's in it for you?
- Hands-on exposure to AI governance in a leading wealth management environment
- Opportunity to work on cutting-edge AI use cases (GenAI, agentic systems)
- Direct mentorship from senior AI risk leadership
- Strong foundation for growth into:
- AI Risk Lead roles
- Model Risk / Validation positions
- AI Governance or Data Risk leadership
