Sr. Model Governance Manager
M
MitekAI/ML governance
Listing location: United States; Workplace type: RemoteFull-TimeManager
Salary160,000 - 200,000 USD per year
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Job Details
- Experience
- 7+ years of experience in model governance, model validation, analytics, or related engineering roles, including 2+ years of direct people management.
- Required Skills
- PythonSQLPeople ManagementJiraConfluence
Requirements
- Bachelor’s degree or higher in Computer Science, Engineering, Statistics, Mathematics, or a related technical or quantitative field.
- 7+ years of experience in model governance, model validation, analytics, or related engineering roles.
- 2+ years of direct people management experience.
- Experience setting team priorities, coaching technical professionals, managing performance, and delivering work across multiple stakeholders.
- Experience overseeing model documentation, approval workflows, controls, lifecycle tracking, and audit or governance reviews.
- Strong understanding of AI/ML development, deployment, validation, benchmarking, and monitoring.
- Experience using documentation and tracking tools to manage governance activities.
- Awareness of data privacy and security considerations.
- Experience managing projects and workflows in Jira.
- Experience managing documentation in Confluence.
- Preferred: Working knowledge of Python and/or Java, SQL, databases, and CSV and JSON data formats.
- Preferred: Understanding of software development lifecycles, SDKs, APIs, integration patterns, and AWS, Azure, or GCP.
- Preferred: Experience evaluating fraud, risk, or decisioning models.
Responsibilities
- Lead and develop model governance professionals through hiring, onboarding, coaching, performance reviews, and career development.
- Set team goals, assign work, balance workloads, and manage capacity to meet governance milestones and business priorities.
- Review team deliverables for accuracy, completeness, and consistency, and resolve day-to-day delivery issues.
- Oversee model governance documentation, including Model Cards, intended use, assumptions, limitations, risks, explainability, and evaluation metrics.
- Maintain visibility into review status and establish metrics for completeness, timeliness, documentation quality, and issue resolution.
- Prioritize model governance reviews and communicate deadline impacts when priorities change.
- Ensure model data lineage and training, validation, and monitoring dataset records are maintained.
- Partner with model owners and the observability team to define monitoring responsibilities, review triggers, and escalation paths.
- Coordinate priorities and dependencies with engineering, data science, risk, compliance, and product teams.
- Improve governance workflows, templates, tools, and automation within established policies.
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