AI Model Validation Lead, GenAI

Requirements

• Master's or PhD in Computer Science, Statistics, Mathematics, Engineering, or a related field.
• 10+ years of experience in AI/machine learning model validation, model risk management, or related roles.
• Expertise in supervised and unsupervised learning techniques, deep learning, natural language processing, and statistical modeling.
• Proficiency in programming languages such as Python, R, or Java, and familiarity with ML frameworks .
• Strong knowledge of data governance, ethical AI principles, and regulatory requirements impacting AI models (e.g., GDPR, Fair Lending, Basel II/III).
• Experience with model validation tools, version control, and documentation best practices.
• Excellent analytical, problem-solving, and written communication skills.
• Ability to manage multiple projects and deadlines, working both independently and as part of a team.
• Prior experience in insurance and/or financial services, healthcare, or regulated industries.
• Certification in model risk management or AI ethics (e.g., SAS Certified AI & Machine Learning Professional).
• Demonstrated ability to lead complex validation projects and mentor junior team members.
• #LI-AIG
• At AIG, we value in-person collaboration as a vital part of our culture, which is why we ask our team members to be primarily in the office. This approach helps us work together effectively and create a supportive, connected environment for our team and clients alike.
• Enjoy benefits that take care of what matters
• Reimagining insurance to make a bigger difference to the world
• American International Group, Inc. (AIG) is a global leader in commercial and personal insurance solutions; we are one of the world's most far-reaching property casualty networks. It is an exciting time to join us - across our operations, we are thinking in new and i

Benefits

Additional Information

AI Model Validation Lead Be part of something groundbreaking At AIG, we are making long-term investments in a brand-new, innovative Generative AI team, designed to explore new possibilities for how artificial intelligence can be applied in insurance and beyond, and we need your help. With the support and investment needed to explore new frontiers in generative AI, you'll be working alongside talented colleagues, innovating and leading projects that will transform how we manage risk and serve our customers. This team is central to our vision of the future and the core of our business offering. We will incorporate best-in-class engineering and product management principles and your guidance and collaboration will be critical to its success. To rapidly advance and innovate, we need your skills and expertise to build world-class products. If you're excited by the opportunity to create meaningful impact at scale, we'd love to hear from you. How you will create an impact This role is responsible for ensuring the accuracy, robustness, and ethical compliance of artificial intelligence models deployed across the organization. This role requires a deep understanding of statistical modeling, machine learning algorithms, and risk management frameworks. The ideal candidate will work closely with data scientists, engineers, and business stakeholders to independently assess and validate models, document findings and recommend improvements. The position demands a strong analytical mindset, excellent communication skills, and the ability to manage multiple projects simultaneously in a fast-paced environment. Design and execute comprehensive model validation strategies, including quantitative and qualitative assessments. Review model development documentation, code, and datasets for correctness, completeness, and compliance with regulatory standards. Conduct independent testing of AI models, evaluating performance metrics, sensitivity analyses, and stress testing outcomes. Actively challenge assumptions, data quality, and methodology used to build and implement models Identify and communicate model risks, limitations, and potential biases to stakeholders, ensuring transparency and accountability. Collaborate with cross-functional teams to refine modeling approaches and support continuous improvement. Prepare clear validation reports and audit trails for review and internal governance.

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