About the job
As an AI Control Manager - Senior Associate within Client Onboarding & Documentation (CO&D), you will apply your AI/ML technical expertise to assess, monitor, and strengthen controls for AI use cases across our organization. This is a unique opportunity to build a career in AI risk management and governance, one of the fastest-growing fields in technology. You will leverage your programming skills and understanding of AI/ML systems to conduct technical risk assessments, evaluate AI models, and identify potential control gaps. You'll translate complex AI technical concepts into actionable risk insights for business stakeholders. Beyond AI, you will also lead control review initiatives using advanced analytics and data-based testing to enhance the broader control environment, conducting proactive process review and performing root cause analysis. You'll support the end-to-end CO&D organization across WKO (Wholesale KYC Operations) and DDS (Digital Documentation Services), helping to establish best practices for AI governance.
Responsibilities
Conduct technical AI risk assessments for AI/ML use cases, evaluating model design, data quality, algorithmic bias, and potential failure modes
Partner with cross-functional teams to develop and implement AI governance frameworks, policies, and control standards
Perform deep-dive technical reviews of AI solutions to identify risks related to accuracy, fairness, explainability, and security
Translate complex AI/ML technical concepts into clear risk assessments and recommendations for non-technical stakeholders
Collaborate with AI developers, data scientists, and technology teams to understand AI system architecture and identify control requirements
Lead control reviews leveraging advanced analytics, and data-based testing to enhance the control environment and drive efficiency across CO&D operations
Conduct proactive reviews using Alteryx, Python, and analytics tools to identify and address emerging risks in CO&D
Perform root cause analysis and partner with stakeholders to implement corrective actions, synthesizing complex information into clear, concise reports for management
Support root cause analysis when AI-related issues arise and recommend technical remediation approaches
Stay current on emerging AI risks, regulatory developments, and industry best practices in AI governance
Conduct proactive process reviews to identify and address emerging risks.
Perform root cause analysis and partner with stakeholders to implement corrective actions.
Leverage Alteryx, Python, and GenAI/LLM to automate processes and improve risk management.
Qualifications
Minimum
Minimum of 5 years of experience working with AI/ML technologies, data science, or related technical fields
Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related technical field
Strong proficiency in Python for data analysis and automation
Solid understanding of machine learning concepts, algorithms, and model development lifecycle
Ability to evaluate AI/ML models and identify technical risks (e.g., bias, overfitting, data quality issues, model drift)
Experience working with or evaluating AI systems, including understanding of how models are trained, validated, and deployed
Strong analytical and problem-solving skills with attention to detail
Excellent communication skills with the ability to explain technical AI concepts to non-technical audiences
Intellectual curiosity about AI governance, responsible AI, and risk management
Ability to adapt quickly in a fast-paced environment and learn new domains
Strong organizational skills and ability to manage multiple priorities
Preferred
Experience with GenAI, large language models (LLMs), or agentic AI systems
Prior exposure to risk management, controls, compliance, or audit functions
Knowledge of financial services, AML/KYC processes, or regulatory environments
Experience with Alteryx or other analytics automation tools
Familiarity with AI governance frameworks, model risk management, or responsible AI principles
Experience with model validation, model monitoring, or AI quality assurance
Master's degree in a technical field
Understanding of API integration and data pipeline development