Staff Machine Learning Model Risk Specialist

Role overview

Help design and operate a model risk management program for a regulated financial institution with a broad inventory of statistical, machine learning, and generative AI applications. You will independently assess model risk, provide effective challenge, strengthen governance, and explain technical findings to audiences ranging from developers and risk specialists to senior stakeholders and regulators. The work spans lending as well as fraud, compliance, finance, capital and liquidity, servicing, and operational risk.

Responsibilities

- Maintain and review model and generative AI inventories, risk assessments, documentation, monitoring reports, and governance materials. - Evaluate methodologies, assumptions, data inputs, system designs, performance measures, controls, limitations, and remediation needs. - Apply a risk-based approach that reflects the purpose, complexity, technology, and risk profile of each model or AI application. - Partner with machine learning teams, developers, business owners, and risk stakeholders to identify emerging risks and improve controls. - Assess model monitoring, fairness, explainability, validation evidence, and ongoing performance, escalating material concerns when appropriate. - Translate complex technical subjects into clear, decision-useful communication while balancing transparency with protection of intellectual property. - Contribute analyses and recommendations that support internal risk management, external validation, and regulatory credibility.

Requirements

- PhD in statistics, econometrics, finance, mathematics, or a related quantitative discipline. - At least five years of experience in model risk management, model governance, machine learning, data science, risk, trust and safety, or technical writing. - Familiarity with generative AI evaluation, prompt and system design, retrieval-augmented generation, tool use, guardrails, and monitoring. - Experience assessing models outside credit underwriting, including applications in fraud, compliance, finance, capital and liquidity, servicing, operational risk, or reporting. - Advanced coding ability in R, Python, and SQL, with experience using Git. - Strong communication, judgment, initiative, and ability to adapt explanations to different audiences. - Understanding of model monitoring, fairness, explainability, and advanced AI/ML risks.

Nice to have

- Knowledge of consumer lending, credit risk, model fairness, explainability, or AI/ML and generative AI in regulated environments. - Experience building or expanding model governance programs in a banking or financial-services setting.

Benefits and work setup

This is a remote role with periodic in-person collaboration and potential travel. The source describes location-dependent compensation and benefits that may include performance incentives, equity, retirement contributions, health coverage, paid leave, wellness and productivity resources, and employee community programs.