Join the LLR family of private equity-backed growth companies.

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.
  
  

What Experience You Will Need

• 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.
  
  

What could set you apart

• 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.