Maintain the enterprise model inventory, including model owners, purpose, risk rating, and deployment status
Apply model risk ratings and confirm required governance steps are completed
Coordinate model release approvals, evidence collection, and sign off processes
Manage versioning, archiving, and traceability for models and related documentation
Ensure models have complete and standardized documentation, such as Model Cards
Document model intent, assumptions, limitations, and known risks
Capture explainability considerations and any human in the loop processes used
Maintain data lineage and dataset information for training, validation, and monitoring
Ensure required validation evidence is available, including performance and testing results
Support stress testing or review activities for higher risk models
Help set up and support ongoing model monitoring activities
Trigger governance reviews when model, data, or scope changes occur
Partner with engineering, data science, risk, compliance, and governance teams
Produce clear and consistent governance documentation for both technical and non-technical audiences
Support the preparation and review of datasets used for model testing and validation
Support or participate in governance reviews, validations, or review meetings

Requirements

Bachelor’s degree, or higher, in a technical or quantitative field (Computer Science, Engineering, Statistics, Mathematics, or similar)
Knowledge, skills and abilities typically gained through 5+ years of experience in model governance, model validation, analytics, or related engineering roles
Experience managing model documentation, approvals, controls, or lifecycle tracking
Understanding of AI/ML models and how they are developed, tested, and used
Familiarity with model validation concepts and benchmarking approaches
Working knowledge of programming languages such as Java and Python
Familiarity with common data formats such as CSV and JSON
Experience writing or reviewing SQL and querying databases, including working with datasets for testing or validation
Understanding of application development concepts and software development lifecycles
Familiarity with SDKs, APIs, and integration patterns used in production systems
Experience working with or supporting systems deployed on cloud platforms (e.g., AWS, Azure, or GCP)
Ability to review technical implementations to understand how models are built, deployed, and monitored
Experience using documentation or tracking tools to support governance, review, or audit activities
Awareness of data privacy and security considerations when working with model data and datasets.

Benefits

Wellness: Universal, supplemental, and private healthcare plan choices based on country specifics
Financial future: retirement/pension plan contributions, MTK stock plan participation
Income protection: life event & disability coverage
Paid time off: generous annual leave, company holidays, volunteer time off
Learning: e-learning license, tuition reimbursement, hackathons
Home office setup allowance
Additional/optional benefits: pet insurance, identity theft protection, legal assistance

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

model governancemodel validationanalyticsAI/ML modelsprogramming languagesJavaPythonSQLdata formatsCSV

Soft Skills

communicationcollaborationdocumentationorganizational skillsattention to detail