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Senior Director, Model Risk Management
About the position
Equifax is where you can power your possible. If you want to achieve your true potential, chart new paths, develop new skills, collaborate with bright minds, and make a meaningful impact, we want to hear from you. Equifax is seeking a seasoned and strategic leader to join our Data and Analytics Center of Excellence as the Senior Director, Model Risk Management. This is a critical leadership role responsible for independently validating and supervising a portfolio of statistical and advanced machine learning models and analytical solutions. This individual will lead a highly talented team of validators and serve as a key consultant on model risk matters across the organization. Equifax has a hybrid work schedule that allows for 2 days of remote work (Monday and Friday), with 3 days onsite (Tuesday, Wednesday, Thursday) every week. This role reports to our office in Alpharetta, Georgia OR midtown ATL (OAC). This position does not offer immigration sponsorship (current or future) including F-1 STEM OPT extension support. This is a direct-hire role and is not open to C2C or vendors. What you will do Leadership & Management Lead, manage, and mentor a high-performing team of data scientists and reviewers, providing expert oversight for both Generative AI application review and rigorous model validation projects. Oversee the execution of multiple complex validation projects simultaneously, from test design to final delivery and communication. Serve as the primary technical lead for validating GenAI safety. Direct team members in identifying and mitigating complex risks such as model hallucinations, prompt injection, and data leakage. Foster a culture of critical thinking, continuous improvement, and effective risk management within the team. Collaborate with global partners to supervise validation projects, ensuring a consistent and technically sound approach to AI/Model Risk Management across non-direct reporting lines Technical Validation & Research Lead and perform independent, deep-dive validations of complex models, focusing on the end-to-end design, implementation, and performance of Generative AI applications alongside traditional statistical models. Conduct deep-dive research into emerging analytical techniques and \"LLM-as-a-judge\" evaluation methods to stay ahead of the curve in validating Agentic AI and other non-deterministic systems. Develop and execute comprehensive validation test designs to assess model soundness and identify potential risks. Critically assess the completeness and accuracy of model documentation, code, and marketing materials. Develop and implement innovative validation approaches for complex and nontraditional models, including those with unstructured data and unique risk profiles. Governance & Collaboration Serve as a trusted advisor to model developers, engineers, and business owners, providing expert guidance on model design, development and AI safety-by-design. Review and provide guidance on model monitoring plans and ongoing performance reports. Drive the enhancement of Model Risk Management procedures and standards to align with evolving regulatory requirements and industry best practices Collaborate with key stakeholders across the organization, including marketing, technology, legal, compliance, and business owners, to ensure a robust model risk governance framework. Respond to and manage inquiries from clients, internal auditors, and regulators regarding model risk matters.
Responsibilities
- Lead, manage, and mentor a high-performing team of data scientists and reviewers, providing expert oversight for both Generative AI application review and rigorous model validation projects.
- Oversee the execution of multiple complex validation projects simultaneously, from test design to final delivery and communication.
- Serve as the primary technical lead for validating GenAI safety.
- Direct team members in identifying and mitigating complex risks such as model hallucinations, prompt injection, and data leakage.
- Foster a culture of critical thinking, continuous improvement, and effective risk management within the team.
- Collaborate with global partners to supervise validation projects, ensuring a consistent and technically sound approach to AI/Model Risk Management across non-direct reporting lines
- Lead and perform independent, deep-dive validations of complex models, focusing on the end-to-end design, implementation, and performance of Generative AI applications alongside traditional statistical models.
- Conduct deep-dive research into emerging analytical techniques and \"LLM-as-a-judge\" evaluation methods to stay ahead of the curve in validating Agentic AI and other non-deterministic systems.
- Develop and execute comprehensive validation test designs to assess model soundness and identify potential risks.
- Critically assess the completeness and accuracy of model documentation, code, and marketing materials.
- Develop and implement innovative validation approaches for complex and nontraditional models, including those with unstructured data and unique risk profiles.
- Serve as a trusted advisor to model developers, engineers, and business owners, providing expert guidance on model design, development and AI safety-by-design.
- Review and provide guidance on model monitoring plans and ongoing performance reports.
- Drive the enhancement of Model Risk Management procedures and standards to align with evolving regulatory requirements and industry best practices
- Collaborate with key stakeholders across the organization, including marketing, technology, legal, compliance, and business owners, to ensure a robust model risk governance framework.
- Respond to and manage inquiries from clients, internal auditors, and regulators regarding model risk matters.
Requirements
- Master’s degree in a quantitative field such as statistics, data science, computer science, mathematics, economics, or finance. A PhD is strongly preferred.
- 7- 10 years of industry experience in predictive modeling, data science, or a related quantitative field.
- Prior experience with credit risk model development and/or validation is highly preferred.
- A minimum of 3 years of experience managing a highly talented team with 6 or more direct reports is preferred.
- Strong knowledge of and hands-on experience with a broad spectrum of modeling techniques, ranging from traditional statistical methods (e.g., Logistic Regression, Time Series, XGBoost) to advanced architecture including Deep Neural Networks. Generative AI, and Agentic AI frameworks are highly preferred.
- Extensive experience with Big Data environments and advanced computational processes.
- Demonstrated experience with model risk management and/or compliance is highly preferred.
- Proven ability to quickly grasp complex concepts and identify potential model issues or validation gaps.
- Proficiency with programming languages such as SAS, SQL, R, Python, Scala, and Spark.
- Hands-on experience with UNIX/LINUX and Google cloud environments.
Nice-to-haves
- Experience as a Data Scientist in the banking industry.
- Exceptional critical thinking, problem-solving, and analytical skills.
- Excellent interpersonal, networking, verbal, and written communication skills, with a proven ability to write and edit high-quality technical reports.
- Highly detail-oriented, proactive, and efficient.
Benefits
- comprehensive compensation and healthcare packages
- 401k matching
- paid time off
- organizational growth potential through our online learning platform with guided career tracks
