Senior Model Validation Analyst - AI

Senior Associate - Model Validation and AI Governance New York City - Hybrid As part of a dynamic AI and Data function, you will play a key role in ensuring that model development and validation activities meet high technical and governance standards. You’ll collaborate closely with both technical teams and business stakeholders, using your communication skills to help ensure best practices are followed throughout the model lifecycle. You will contribute to building effective monitoring approaches across a range of use cases, including both structured and unstructured data, with a focus on improving automation and scalability. This position centers on model validation, working alongside model risk and governance teams to interpret requirements and translate them into actionable validation activities-such as bias analysis, stress testing, and root cause investigations-aligned with both internal policies and regulatory expectations. Key Responsibilities Model methodology review: Assess model design choices, data sources, feature engineering, and performance metrics, providing constructive challenge where necessary. Independent validation: Critically evaluate both traditional statistical models and modern AI approaches by replicating results, testing alternatives, and, where appropriate, developing challenger models to assess risk and impact. Reporting and documentation: Produce detailed validation reports alongside clear, accessible summaries for non-technical audiences, using concise language and effective visuals. Regulatory interpretation: Analyse technical documentation and regulatory guidance, converting requirements into practical validation frameworks, checklists, and controls. Ongoing monitoring: Define and track key indicators such as model drift, fairness, stability, and reliability, ensuring alignment with business performance metrics and supporting implementation of alerting mechanisms. Standards development: Help shape reusable frameworks, templates, and testing approaches across predictive, generative, and agent-based AI use cases, while promoting consistency and best practice across teams. Continuous development: Stay informed on emerging trends and advancements in statistics, machine learning, and artificial intelligence. Skills and Experience Education: Postgraduate qualification in a quantitative discipline such as Statistics, Data Science, Computer Science, Mathematics, Economics, or Engineering. Industry experience: Proven experience (typically 4+ years) in model validation, governance, or risk management within regulated environments, covering both statistical and AI/ML models. Technical expertise: Strong understanding of modelling techniques (e.g., regression methods, survival analysis, gradient boosting, neural networks, and feature engineering practices). Programming: Proficient in Python and SQL. Communication: Able to convey complex technical concepts clearly to diverse audiences and work effectively across multidisciplinary teams. Attention to detail: Skilled in interpreting complex documentation and translating requirements into precise and practical deliverables. Mindset: Self-driven, curious, and committed to continuous learning and improvement. Familiarity with evaluating generative AI solutions, including prompt design and retrieval-augmented approaches. Knowledge of model interpretability techniques, stress testing methods, and AI-related privacy and security considerations. Understanding of reinforcement learning concepts. Experience engaging with governance functions such as legal, compliance, or cybersecurity, and converting their requirements into actionable controls. Awareness of common AI applications in regulated industries, along with their benefits, limitations, and potential risks. #J-18808-Ljbffr Apollo Solutions