Sr. Model Governance Manager

Accountabilities:: Lead, develop, and manage a team of model governance professionals, including recruitment, onboarding, coaching, performance reviews, and career development. Establish clear team objectives, allocate responsibilities, balance workloads, and manage capacity to meet governance milestones and business priorities. Review team deliverables for accuracy, completeness, and consistency, providing technical guidance and resolving day-to-day delivery challenges. Build a collaborative and accountable team culture that promotes sound judgment, continuous learning, and timely escalation of concerns. Oversee model governance documentation, ensuring model ownership, purpose, measurements, and supporting records remain accurate and up to date. Ensure standardized model documentation covers Model Cards, intended use, assumptions, limitations, known risks, explainability, and evaluation metrics. Maintain clear visibility into governance review progress for the team, management, engineering, and other relevant stakeholders. Define and monitor performance metrics covering documentation quality, review timeliness, completeness, and issue resolution; communicate progress, risks, and resource needs to leadership. Own the model governance review prioritization queue, balancing stakeholder deadlines, team capacity, and review complexity while communicating the impact of priority changes. Identify and communicate weaknesses in model testing approaches, helping stakeholders understand the confidence and limitations of evaluation metrics. Ensure model data lineage and records for training, validation, and monitoring datasets are maintained. Partner with model owners and observability teams to define monitoring responsibilities, review triggers, and escalation paths, ensuring changes to models, data, or scope receive appropriate governance review. Track overdue reviews and escalate material risks, exceptions, and unresolved issues to the Director and relevant risk or compliance owners. Coordinate governance priorities and dependencies with engineering, data science, risk, compliance, and product teams, representing the function in cross-team discussions. Oversee model review preparation, ensuring supporting evidence is accessible and documentation is understandable to both technical and non-technical audiences. Improve governance workflows, templates, tools, and automation within established policies, and recommend broader process changes to leadership. Develop and defend proposals for improvements that require action or support from other teams. Requirements: Bachelor's degree or higher in Computer Science, Engineering, Statistics, Mathematics, or a related technical or quantitative discipline. At least 7 years of relevant experience in model governance, model validation, analytics, or related engineering roles. At least 2 years of direct people management experience, including setting priorities, coaching technical professionals, managing performance, and coordinating delivery across stakeholders. Demonstrated experience overseeing model documentation, approval workflows, governance controls, lifecycle tracking, and audit or governance reviews. Strong understanding of AI/ML development, deployment, validation, benchmarking, and monitoring, with the ability to assess technical implementations and guide governance decisions. Experience using documentation and tracking tools to manage governance activities, with awareness of data privacy and security considerations. Practical experience managing projects and workflows in Jira and maintaining documentation in Confluence. Strong people leadership skills, including setting clear expectations, providing constructive feedback, and supporting professional development. Excellent communication and presentation abilities, with confidence explaining technical topics, risks, and priorities to diverse audiences. Collaborative approach, sound judgment, strong organizational skills, attention to detail, and the ability to manage competing priorities in risk-aware or regulated environments. Preferred qualifications: Working knowledge of Python and/or Java, SQL, databases, and common data formats such as CSV and JSON. Ability to review datasets and technical evidence used in model testing and validation. Understanding of software development lifecycles, SDKs, APIs, integration patterns, and cloud environments such as AWS, Azure, or GCP. Experience evaluating fraud, risk, or decisioning models. Benefits: Annual salary range of $160,000–$200,000, with individual compensation determined by experience, professional competencies, and geographic differentials. Remote-first working environment, with flexibility to work remotely or from an office where available. Healthcare plan options, including universal, supplemental, and private healthcare coverage depending on location. Retirement or pension plan contributions. Eligibility to participate in a company stock plan. Life event and disability coverage. Generous annual paid leave and company holidays. Volunteer time off. Access to e-learning resources and tuition reimbursement. Opportunities to participate in hackathons and professional development activities. Home office setup allowance. Optional benefits may include pet insurance, identity theft protection, and legal assistance.