Client Onboarding and Documentation Control Management Associate

Help shape how a global organization manages risk in a rapidly evolving, technology-driven environment. You will use analytics and responsible artificial intelligence to strengthen controls, reduce manual effort, and improve outcomes. You will partner with leaders across the business and influence change beyond your immediate team. Join us to build a modern control program that supports innovation, accountability, and continuous improvement.

As a Client Onboarding and Documentation Operations Control Management –  Associate in the Operations Control Management team, you will promote the controls agenda by identifying, assessing, and monitoring key risks while ensuring adherence to operational policies, control standards, and legal and regulatory requirements. You will evaluate and enhance the control framework for artificial intelligence use cases across the organization. You will lead data-driven control reviews, perform proactive assessments, and deliver clear, actionable recommendations that strengthen the overall control environment. You will work in a collaborative culture that values partnership, thoughtful challenge, and measurable impact.

You will be part of a cross-business program modernizing the client onboarding and documentation experience through a more consistent, standardized approach. Your work will help embed governance and best practices for using artificial intelligence in day-to-day activities. You will also help teams adopt smarter testing methods and more automated controls, improving both efficiency and risk coverage. This is an opportunity to build expertise at the intersection of controls, analytics, and responsible technology.

Job responsibilities

• Lead control reviews using analytics and data-driven testing to strengthen the control environment and improve operational efficiency
• Perform proactive monitoring and testing to identify emerging risks and support timely issue identification and resolution
• Conduct root-cause analysis for control issues, including issues driven by Technology including artificial intelligence-related issues, and partner on corrective actions and sustainable remediation
• Automate control testing where appropriate to reduce manual effort and improve consistency and traceability
• Execute risk and control assessments for artificial intelligence solutions, covering model design, data quality, bias and fairness, security, accuracy, and potential failure modes
• Partner with stakeholders across compliance, legal, audit, data, oversight, and onboarding teams to implement governance frameworks, policies, and control standards
• Maintain awareness of evolving AML/KYC risks, holistic operational risks, including AI risks, regulatory expectations, and industry best practices, and incorporate them into the controls agenda
• Influence and drive control improvements across teams beyond direct reporting lines in a dynamic, deadline-driven environment

Required qualifications, capabilities, and skills

• Bachelor’s degree
• Minimum 6 years of experience in financial services
• Knowledge of client onboarding processes, including Anti-Money Laundering and Know Your Customer expectations
• Experience performing data-driven testing and end-to-end control reviews, including risk assessment and control enhancement recommendations
• Experience in risk management, controls, compliance, or audit within a regulated environment
• Strong communication and interpersonal skills, with the ability to build relationships across levels and present findings clearly
• Ability to deliver high-quality results under tight deadlines in a complex, fast-moving environment

Preferred qualifications, capabilities, and skills

• Experience evaluating and applying artificial intelligence and machine learning solutions (including generative artificial intelligence, large language models, or agent-based systems) in a risk, controls, or governance context
• Understanding of the machine learning lifecycle (training, validation, deployment) and key model risk topics such as data quality, bias and fairness, explainability, and monitoring
• Proficiency in Python for analytics, automation, and control testing
• Experience with analytics and workflow automation tools such as Alteryx (or similar) to streamline testing and evidence collection