Analyst II - Information Management, AI Governance

NYU Langone Health • New York, NY

2d

About The Position

The AI Governance Analyst supports the safe, legal, and responsible development of AI/ML applications across NYU Langone Health. This role leads the intake of AI governance reviews, coordinates the review and preparation of risk reports and recommendations, supports submissions for AI/ML governance reviews, and communicates decisions. In addition, this role develops tools and processes that strengthen governance practices and lifecycle monitoring. The analyst acts as a bridge between mission areas, technical teams, and AI/ML governance at NYU Langone Health, promoting transparency, accountability, and alignment with institutional policies and national frameworks such as the NIST AI RMF and NYS-P24-001.

Requirements

• 1+ years of experience in data engineering, data analysis, or applied informatics, with demonstrated ability to translate technical work into operational impact
• Bachelor's degree in data science, mathematics, engineering, bioinformatics, or a related field; or equivalent combination of education and relevant experience
• Proficient in Python, SQL, R, and JSON, with hands-on experience supporting data pipelines, controls monitoring, and audit workflows
• Strong grasp of machine learning evaluation metrics, including ROC-AUC, F1 score, precision/recall, calibration curves, and other benchmarking methods used in comparative model analysis
• Experience working with structured templates and tools to support monitoring, bias auditing, explainability, and validation across the AI lifecycle
• Deep commitment to ethical AI, transparency, and lifecycle stewardship ensuring models remain accurate, accountable, and aligned with institutional and public trust
• Ability to collaborate across diverse teams with strong organizational and communication skills
• Familiarity with AI oversight frameworks including NIST AI RMF, NYS-P24-001, and OMB M-24-10
• Qualified candidates must be able to effectively communicate with all levels of the organization.

Nice To Haves

• Use tools such as Python or SQL to support intake analytics, metadata quality checks, and validation readiness.

Responsibilities

• Guide staff through the full lifecycle of AI/ML use case intake, including submission support, intake training, and lifecycle tracking.
• Support risk assessments and governance reviews by scheduling key stakeholders, maintaining intake logs, organizing use case materials, and preparing committee materials.
• Coordinate with enterprise teams to maintain a robust AI/ML inventory across pilot, production, and retirement phases.
• Ensure completeness and accuracy of intake records and documentation, leveraging automated tracking tools and, in some cases, structured data formats.
• Serve as an AI/ML governance liaison between clinical, research, operational, and engineering teams, ensuring transparency across the full AI lifecycle.
• Prepare comprehensive AI/ML documentation packages for review, including use case summaries, risk triggers, evaluation outputs, and validation evidence.
• Support committee presentations by translating technical model details into digestible insights for diverse stakeholders.
• Communicate governance decisions, required actions, and timelines with clarity and accountability.
• Participate in document and share tools that enhance AI/ML governance, including tracking dashboards, model evaluation utilities, and monitoring templates.
• Monitor and maintain centralized governance systems tracking intake status, model health, and audit trails.
• Use technical skills (Python, SQL, JSON) to automate and scale governance functions and testing/validation methods.
• Partner with data engineers and domain experts to co-design training materials and technical infrastructure that promote sustainable, legal, and ethical AI use across NYULH.
• Maintain best-practice templates and reference materials for risk mitigations and safeguard implementation, benchmarking and monitoring calculations using metrics like ROC-AUC, F1 score, and precision/recall, calibration and drift-detection planning, data quality and output control protocols, and algorithmic bias assessments.
• Collaborate with data science and informatics teams to integrate monitoring logic using Python, SQL, or R.
• Provide operational support for annual reviews, model audits, and subgroup performance assessments.
• Apply familiarity with evaluation methods and NIST AI RMF to ensure risk model oversight.

Benefits

• financial security benefits
• a generous time-off program
• employee resources groups for peer support
• holistic employee wellness program, which focuses on seven key areas of well-being: physical, mental, nutritional, sleep, social, financial, and preventive care.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level