Lead Operational and AI Risk Manager

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JOB DESCRIPTION

This role can work remotely anywhere in the United States.

Job Summary

The Lead Operational Risk Manager is a versatile risk professional who supports a range of analytical, oversight, and regulatory priorities within the Non-Financial Risk organization. The role uses data, sound judgment, and cross-functional partnership to help identify important risks, evaluate whether business practices and safeguards are working as intended, and turn complex information into clear recommendations for risk leadership and executive stakeholders.

The position provides leadership through expertise, influence, project leadership, and cross-functional collaboration.

This position is well suited for someone who is curious, adaptable, and comfortable moving between different business problems. The successful candidate will independently lead complex analytical and risk oversight initiatives, exercise significant judgment in ambiguous situations, and develop practical, sustainable solutions to complex business and regulatory challenges. The role also includes AI-related model oversight and supports responsible AI adoption across the risk function.

Key Responsibilities

Data Analysis and Management Reporting

Find, organize, and analyze data from multiple sources to identify trends, anomalies, and areas requiring management attention.

Develop clear reports, dashboards, metrics, and presentations for risk leaders and senior management.

Help strengthen the use of objective evidence and measurable outcomes when assessing risk and the effectiveness of business safeguards.

Work with business and technology partners to understand data needs, confirm data quality, and improve access to useful information.

Risk Reviews and Business Partnership

Assess significant business processes, operational activities, and changes to identify potential risks and practical opportunities for improvement.

• •Lead cross-functional initiatives involving Risk, Technology, Compliance, Audit, and business stakeholders to support regulatory commitments, remediation activities, and strategic objectives.
• •Provide consultation and guidance to business and risk stakeholders regarding complex risk management, analytics, model oversight, and control-related matters.

Evaluate whether policies, procedures, monitoring, and other safeguards are appropriately designed and operating as intended.

Partner with business teams to understand issues, challenge assumptions constructively, and develop practical recommendations.

Support the resolution of identified issues and help management track important commitments through completion.

Model and Analytical Tool Reviews

Support independent reviews of models and analytical tools used to make or inform business decisions.

Analyze model design, inputs, outputs, performance, limitations, and ongoing monitoring using a risk-based approach.

Prepare clear documentation that explains the work performed, conclusions reached, and any recommended improvements.

Collaborate with model owners, data teams, and technical specialists to resolve questions and improve ongoing oversight.

AI Model Validation and Responsible AI Adoption

Lead the independent validation of a regulatory model with AI and LLM components, including assessment of model design, inputs, outputs, performance, and known limitation, while serving as a key subject matter expert on model governance, validation methodologies, and responsible AI practices.

Apply structured LLM evaluation methods — including benchmark testing, output consistency checks, adversarial prompting, and bias assessment — to evaluate model reliability and fitness for regulatory use.

Prepare validation documentation that clearly explains methodology, findings, limitations, and recommended actions in a format suitable for regulatory review.

Actively use AI tools in day-to-day risk work — including analysis, drafting, and research — and identify opportunities to improve the efficiency and quality of risk oversight through AI-enabled workflows.

Assess emerging AI tools and associated risks, contributing to governance considerations and practical implementation guidance as the function’s AI capabilities mature.

Fraud Risk and Program Effectivenes s

Assess fraud prevention, detection, monitoring, and response practices using available data and performance information.

Analyze fraud trends and outcomes to identify gaps, emerging concerns, and opportunities to strengthen the program.

Help translate fraud data and operational observations into clear conclusions and recommendations for management.

Regulatory and Strategic Priorities

Support regulatory examinations, internal reviews, remediation efforts, and other time-sensitive risk initiatives.

Conduct focused research and analysis on emerging or complex business issues.

Provide flexible support across the Risk organization as priorities evolve, while maintaining accountability for high-quality and timely work and independently driving complex initiatives from problem definition through recommendation and implementation support.

Build productive working relationships across business, technology, compliance, audit, and risk teams.

Required Qualifications

Bachelor's degree in business, finance, economics, mathematics, statistics, data analytics, information systems, risk management, or a related field.

At least 7 years of relevant experience in risk management, fraud, model oversight, data analytics, or a related discipline.

Experience in banking, fintech, payments, or another regulated financial services environment.

Demonstrated ability to locate, interpret, and analyze data and translate findings into practical business insights.

Strong problem-solving skills and the ability to work independently through ambiguous or complex assignments.

Clear written and verbal communication skills, including the ability to explain technical or complex matters to non-technical audiences.

Ability to build effective relationships and work across organizational boundaries in a remote environment.

Comfortable using AI tools as part of everyday work, including large language models and AI-assisted research or analysis tools.

Preferred Qualifications

Experience evaluating business processes, controls, or risk management programs.

Experience supporting regulatory examinations, internal audits, or remediation programs.

Experience with fraud analysis, model reviews, statistical analysis, or performance monitoring.

Experience using data analysis, visualization, reporting, or governance tools.

Working knowledge of SQL, Python, Power BI, Tableau, or similar tools is beneficial.

Familiarity with LLM evaluation techniques, including benchmark testing, output consistency assessment, or bias analysis, particularly in a model validation or oversight context.

Experience using AI tools in day-to-day work, with a demonstrated interest in leveraging AI to improve analytical output and amplify the impact of the Risk organization.

Core Capabilities

Analytical and data-minded

Intellectual curiosity and willingness to learn

Adaptable and comfortable with change

Ownership and follow-through

Sound judgment and practical problem solving

Collaborative stakeholder management

• •Clear and concise communication
• •AI-forward

• •Ability to manage competing priorities
• •Cross-functional influence without direct authority

• ** Ability to work in the U.S. without sponsorship**
• **Ability to meet the location requirement outlined above**

POSITION TYPE

Regular

PAY RANGE

The targeted full-time base salary for this position is $97,100 to $145,500 per year. The final compensation will be determined by a number of factors such as qualifications,