Senior Associate - Model Validation and AI Governance

New York Life•New York, NY

4d•$124,000 - $177,000•Hybrid

About The Position

As part of the Artificial Intelligence & Data (AI&D) organization, you will: Apply your communications skills, working closely with both technical partners and business stakeholders to ensure proper modeling processes are followed during the model development stage. Help design robust ongoing monitoring plans for both structured and unstructured data use cases, while helping to automate the monitoring process. This role focuses on model validation, where you’ll work closely with the Model Risk Management (MRM) team, to translate requirements into practical controls and evaluations (e.g., bias testing, stress testing, root cause analysis) by leveraging technical and regulatory documents/procedures.

Requirements

Education: Advanced degree in Statistics, Computer Science, Data Science, Mathematics, Economics, Engineering or a related quantitative field with a sound knowledge of statistics.
Experience: 4+ years in model validation, model governance, or model risk management for predictive and AI/ML models within regulated environments.
Technical skills: Hands‑on experience with and theoretical understanding of statistical modeling and ML (e.g., linear/logistic regression, survival analysis, GLM, GBM/XGBoost, GAMs, neural nets, feature engineering/selection, etc).
Programming: Proficiency in Python and SQL
Strong communication skills and a collaborative mindset
Attention to detail: Demonstrated ability to read technical regulatory documents and procedures and accurately capture key requirements, constraints, and obligations.
Proactive mindset and commitment to continuous learning

Nice To Haves

Experience evaluating Generative AI systems (e.g., prompting strategies, RAG evaluation).
Knowledge of stress testing, model interpretability, privacy and security considerations for AI systems.
Conceptual understanding of Reinforcement Learning
Experience collaborating with cross‑functional review bodies (e.g., Legal, Compliance, Cybersecurity) and translating governance requirements into practical evidence and controls.
Applied AI literacy: Sound conceptual and practical understanding of common AI use cases across financial/insurance industries (e.g., underwriting support, claims triage, fraud detection, marketing and sales enablement, agent productivity, customer service), including strengths, limitations, and failure modes.

Responsibilities

Methodology review and challenge: Review model design and selection, data pipelines, features, and evaluation metrics.
Parallel validation of traditional and AI models: Effectively challenge model developers on their decisions. Explore and compare alternative methods, replicate key results, build challenger models when appropriate, and assess business impact and risk.
Documentation & reporting: Write rigorous validation reports and create plain‑language executive summaries that break down complex problems into easy‑to‑understand narratives and visuals.
Risk & controls translation: Read and interpret technical regulatory documents and procedures. Capture key requirements and translate them into validation checklists, standard operating procedures, and control evidence.
Post‑deployment monitoring: Define success criteria for drift, bias/fairness, stability, hallucinations focusing on business KPIs, and partner with teams to operationalize alerts.
Standards & enablement: Contribute to common templates, playbooks, guidelines and reusable test harnesses for predictive, generative, and agentic AI use cases; share best practices across data science, engineering, and product teams.
Stay up-to-date on statistical/ML/AI trends and research.