Senior AI/ML Model Validation & Governance Specialist

Resourceful Talent Group•New York, NY

3d•$0 - $210,000•Hybrid

About The Position

We are seeking an experienced AI/ML Model Validation and Governance professional to join a highly established financial services organization in New York. This is a full-time hybrid opportunity based in New York City (3 days onsite per week) offering a base salary of up to $210,000, a 10% annual bonus, and a sign-on bonus. This position will independently evaluate predictive, machine learning, Generative AI, and other advanced analytical models. The ideal candidate combines strong quantitative skills, hands-on Python and SQL experience, and a thorough understanding of model risk management within a regulated environment.

Requirements

Advanced degree in mathematics, statistics, computer science, data science, engineering, economics, or another quantitative discipline.
At least 4 years of direct experience in model validation, model risk management, or AI/ML governance.
Hands-on experience independently validating predictive or AI/ML models.
Strong Python and SQL skills.
Experience with benchmarking, explainability, bias and fairness testing, sensitivity analysis, and ongoing model monitoring.
Understanding of model risk management principles and regulatory expectations.
Experience working in financial services, insurance, banking, or another regulated industry.
Strong written and verbal communication skills.
Ability to work onsite in New York City three days per week.

Nice To Haves

Experience validating Generative AI, LLM, NLP, or RAG-based solutions.
Familiarity with AI governance and responsible AI frameworks.
Experience with structured and unstructured datasets.
Knowledge of SR 11-7 or comparable model risk management guidance.
Experience presenting validation findings to senior stakeholders or governance committees.

Responsibilities

Perform independent validation of predictive, statistical, AI/ML, and Generative AI models.
Evaluate model methodology, assumptions, data quality, implementation, performance, limitations, and intended use.
Conduct benchmarking, sensitivity analysis, stress testing, and performance testing.
Assess model explainability, bias, fairness, robustness, and regulatory compliance.
Review models using structured and unstructured data, including NLP, LLM, and RAG-based solutions.
Evaluate model monitoring frameworks, performance thresholds, and governance controls.
Document findings clearly and communicate recommendations to model developers, business stakeholders, risk teams, and senior management.
Support the continued development of AI governance and model risk standards.