Staff Machine Learning Model Risk Specialist in New York at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Machine Learning Model Risk Specialist based in the United States.

This is a high-impact opportunity to help shape model risk management within a growing, technology-driven banking environment.
You’ll evaluate sophisticated machine learning models, traditional quantitative models, and emerging Generative AI applications.
The role combines technical analysis, risk governance, regulatory readiness, and cross-functional partnership.
You’ll work closely with Machine Learning, Engineering, Business, Legal, Compliance, and other risk teams.
Your expertise will help ensure models are appropriately documented, monitored, challenged, and governed.
You’ll also help establish practical approaches for managing risks associated with rapidly evolving AI technologies.
The position is remote-first, with periodic opportunities for in-person collaboration and team engagement.

• Independently execute core elements of the model risk management program, supporting a diverse and growing inventory of models and Generative AI applications across banking, lending, fraud, compliance, finance, capital, liquidity, servicing, and operational risk.
• Partner with Machine Learning teams, AI developers, business sponsors, and risk stakeholders to maintain accurate model inventories, risk assessments, documentation, monitoring reports, and governance materials.
• Review model methodologies, assumptions, data inputs, system designs, performance measures, controls, limitations, and other relevant factors to provide effective challenge and identify potential risks or remediation needs.
• Apply a risk-based methodology when assessing technologies ranging from traditional statistical and financial models to advanced machine learning systems and Generative AI applications.
• Conduct and document model risk assessments, monitoring reviews, and targeted quantitative analyses in support of internal policies and regulatory expectations.
• Help develop practical governance frameworks for emerging technologies, particularly machine learning and Generative AI where risk-management practices and evaluation standards continue to evolve.
• Translate complex technical and quantitative concepts into clear, decision-useful insights for technical and non-technical stakeholders, including senior leaders and external parties.
• Support responses to questions from regulators, lending partners, and other external stakeholders in collaboration with relevant business, risk, legal, compliance, and technical teams.
• Track model risk findings, remediation plans, program objectives, and emerging risks while escalating material issues and recommending practical improvements.

• Master’s degree in finance, mathematics, economics, statistics, or another quantitative discipline; a PhD or advanced degree is preferred.
• 4+ years of experience in model risk management, model validation, model governance, machine learning, data science, quantitative risk, AI governance, or a closely related technical risk function.
• Strong understanding of AI/ML methodologies, including approaches such as tree-based models and neural networks, along with general familiarity with Generative AI applications.
• Experience assessing or governing models, preferably in regulated financial services, consumer lending, credit risk, or another high-stakes environment.
• Familiarity with Generative AI evaluation concepts, including prompt and system design, retrieval-augmented generation, tool use, guardrails, and ongoing monitoring.
• Proficiency in R, Python, or comparable programming languages; advanced experience with Python, R, SQL, and Git is a strong advantage.
• Understanding of model monitoring, fairness, explainability, and broader AI/ML risk considerations.
• Ability to evaluate models beyond credit underwriting, including applications supporting fraud, compliance, finance, capital and liquidity, servicing, operational risk, or financial reporting.
• Strong communication skills, with the ability to translate highly technical information into clear recommendations for audiences with different levels of expertise while appropriately managing sensitive or proprietary information.
• Proactive, analytical, and independent approach, with the ability to take ownership, navigate ambiguity, and collaborate effectively across technical and business teams.
• Interest in consumer lending, credit risk, model fairness, explainability, and the responsible use of machine learning and Generative AI in regulated environments.

• Competitive compensation: U.S. remote base salary range of $157,000–$217,500 USD, with actual compensation determined by location, skills, experience, education, and training.
• Additional compensation: Target bonus opportunities and annual equity grants that vest quarterly.
• Retirement benefits: 401(k) or applicable retirement savings plan with a company match of $2 for every $1 contributed, up to $15,000 annually.
• Employee Stock Purchase Plan: Discounted stock purchase opportunities for eligible U.S. employees.
• Healthcare: Comprehensive medical, dental, and vision coverage, plus wellness resources and Health Savings Account contributions for eligible U.S. plans.
• Financial protection: Life insurance and disability coverage.
• Time off: Paid time off, sick leave, and company holidays in accordance with local requirements.
• Family support: Paid family and parental leave, alongside benefits supporting fertility, parenthood, and caregiving.
• Wellbeing: Employee Assistance Program, mental health resources, and an annual wellness allowance.
• Financial wellness: Access to financial planning resources and a financial concierge service for eligible U.S. employees.
• Productivity support: Annual productivity allowance to help employees invest in tools and resources that support effective remote work.
• Remote-first flexibility: Work remotely across the U.S., with most teams meeting in person approximately once or twice per quarter for multi-day collaboration sessions.
• Community and connection: Team events, company-wide gatherings, and employee resource groups, with additional onsite perks available at company offices.
• Inclusive workplace: Commitment to equal opportunity, accessibility, diversity, and fair hiring practices.