Manager – AI Assisted Process, Risks & Controls Transformation - Investment Management

Manager role focused on applying AI to process, risk, and controls transformation within the investment management sector. Responsibilities include identifying, evaluating, and prioritizing risks, designing and implementing AI-enabled governance, using AI for anomaly detection, enhancing process effectiveness with AI, and supporting model risk management for AI models. Requires experience in financial services, investment management processes, and applying AI/advanced analytics to risk and control management.

What you'd actually do

  1. Learn how to identify, evaluate, and prioritize business, operational, regulatory, and technology risks across investment management processes, and apply AI-enabled techniques to improve detection, monitoring, and mitigation of those risks
  2. Support clients in designing and implementing AI-enabled governance across the process, risk and control lifecycle including regulatory and compliance impact assessment, process modeling and risk and control mapping, risk identification, control review, testing and enhancement, and issue management and reporting
  3. Assist with continuous controls monitoring by leveraging AI to perform simultaneous, continuous anomaly detection across transactions, reconciliations, valuations, and other high-volume operational activities to surface discrepancies earlier.
  4. Help deploy AI to enhance process and control effectiveness across the middle/back office processes, including monitoring data flows between systems and data platforms and alerting teams to integration/control issues.
  5. Support the development and operationalization of model risk management (MRM) practices for AI and traditional models across the model lifecycle (development, testing/validation, deployment, monitoring), including clearer roles, accountability, and control environment expectations.

Skills

Required

  • 6+ years of experience in financial services or consulting with exposure to investment management operations, compliance, risk, internal audit, and/or technology/data functions.
  • Demonstrated knowledge of investment management processes (trade lifecycle, reconciliations, collateral/margin, pricing/valuation support, fund/investment accounting, performance/reporting) and where operational risks and controls typically sit.
  • Experience applying AI/advanced analytics to process, risk, and control management
  • Experience or familiarity with MRM and AI governance concepts, including documentation, validation, monitoring, and oversight needed to maintain transparency, fairness, and accountability for AI systems in investment management.
  • Understanding of AI risk considerations (e.g., bias/fairness, reliability, hallucinations, privacy/security, and attribution/copyright risk) and how to mitigate them via governance and controls.
  • Strong project/program management skills and ability to manage multiple priorities and deadlines with high-quality delivery.
  • Strong oral and written communication skills, including supporting proposals and executive-ready presentations.

Nice to have

  • Ability to work independently and collaborate as part of a team
  • Effective written and verbal communication skills
  • Meticulous attention to detail and quality of work product
  • Ability to build and sustain professional relationships
  • Ability to lead projects or workstreams
  • Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
  • Strong interpersonal skills and professional demeanor
  • Ability to meet deadlines
  • Ability to mentor and provide clear guidance to others

What the JD emphasized

  • AI-enabled techniques
  • AI-enabled governance
  • leveraging AI
  • deploy AI
  • AI and traditional models
  • model risk management (MRM)
  • AI governance concepts
  • AI risk considerations

Other signals

  • AI-enabled techniques to improve detection, monitoring, and mitigation of risks
  • AI-enabled governance across the process, risk and control lifecycle
  • leveraging AI to perform simultaneous, continuous anomaly detection
  • deploy AI to enhance process and control effectiveness
  • model risk management (MRM) practices for AI and traditional models