Staff Machine Learning Model Risk Specialist
New
J
JobgetherBanking Finance
Based in the United StatesFull-TimeStaff
SalaryU.S. remote base salary range of $157,000–$217,500 USD
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Job Details
- Experience
- 4+ years of experience
- Required Skills
- PythonSQLGitMachine LearningRGenerative AI
Requirements
- Master’s degree in finance, mathematics, economics, statistics, or another quantitative discipline (PhD preferred).
- 4+ years of experience in model risk management, model validation, model governance, machine learning, data science, or quantitative risk.
- Strong understanding of AI/ML methodologies such as tree-based models and neural networks.
- General familiarity with Generative AI applications including prompt design, retrieval-augmented generation, and tool use.
- Experience assessing or governing models in regulated financial services or high-stakes environments.
- Proficiency in R or Python (advanced experience with Python, R, SQL, and Git is preferred).
- Understanding of model monitoring, fairness, explainability, and AI risk considerations.
- Ability to evaluate diverse models including credit, fraud, compliance, finance, and operational risk applications.
- Strong communication skills with the ability to translate technical information for diverse audiences.
- Proactive, analytical, and independent approach with the ability to navigate ambiguity.
Responsibilities
- Independently execute core elements of the model risk management program for a diverse inventory of models and Generative AI applications.
- Partner with Machine Learning teams, AI developers, business sponsors, and risk stakeholders to maintain accurate model inventories and documentation.
- Review model methodologies, data inputs, system designs, and performance measures to identify potential risks or remediation needs.
- Conduct and document model risk assessments, monitoring reviews, and targeted quantitative analyses.
- Develop governance frameworks for emerging technologies including machine learning and Generative AI.
- Translate complex technical and quantitative concepts into clear insights for technical and non-technical stakeholders.
- Support responses to regulatory inquiries in collaboration with internal risk, legal, and compliance teams.
- Track model risk findings and remediation plans while escalating material issues.
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