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.
Job 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.
Required Qualifications, Capabilities and Skills:
- 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 Qualifications, Capabilities and Skills:
- 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