We are seeking a dynamic and detail-oriented GenAI and Model Risk Management Analyst to support the execution of our operational architecture and model risk management (MRM) processes. This role sits at the intersection of data science, risk, and operations, serving as the first line of defense in ensuring that both statistical and generative AI models are developed, implemented, and governed in alignment with internal policies and regulatory expectations.
You will work closely with internal teams and external partners (e.g., fintech firms like Tifin) to guide models through the full lifecycle—from development and documentation to risk review and approval—ensuring transparency, compliance, and operational integrity.
Responsibilities
- Lead and support the model implementation lifecycle, including risk assessments for both statistical and GenAI models.
- Navigate and document operational and architectural risks, ensuring secure data movement and adherence to privacy guardrails.
- Collaborate with Model Risk Management (MRM) and Architectural Risk Review Board (AIRC) teams to ensure timely and thorough model validation and approval.
- Drive the documentation process, producing high-quality, structured reports (40–60 pages) that clearly articulate modeling techniques, assumptions, and risk mitigations, as well as the details of architectural risk highlighting the components of the solution and movement of data through the architecture
- Act as a liaison between business units, legal, compliance, and external partners, ensuring alignment and accountability across stakeholders.
- Apply project management skills to track progress, meet deadlines, and encourage cross-functional teams to complete evaluations and assignments.
- Translate complex modeling concepts into clear, business-friendly language for non-technical stakeholders.
Skills Required
- Strong communication and leadership skills – able to influence, negotiate, and partner effectively under pressure.
- Hands-on experience in model development or implementation – ideally with a background in applied statistics, data science, or decision sciences.
- Understanding of model risk management frameworks – including both statistical and architectural perspectives.
- Excellent documentation and writing skills – capable of producing structured, insightful, and compliant model documentation.
- Understanding of the architecture/operational risk approval process including 3rd party vendor risk management
- Technical proficiency – experience with tools such as Python, R, and statistical libraries; familiarity with Confluence and SharePoint is a plus.
- Understanding and proficiency with Generative AI tools, concepts, architecture and interconnectivity of platforms including the use of LLMs, trust boundaries, prompt tools and RAG data.
Education & Work Experience
- Master’s degree in Data Science, Applied Statistics, Architecture, or a related field.
- Experience in financial services, especially in wealth management or fintech environments.
- Familiarity with cloud environments and data infrastructure, including how data is stored, moved, and secured.
GenAI and Model Risk Management Analyst
Minneapolis, MN
Financial Services
