Risk Model Validation Associate – GenAI & ML

Santander • Boston, MA

2d • $90,000 - $170,000

About The Position

Santander is a global leader and innovator in the financial services industry and is evolving from a high-impact brand into a technology-driven organization. Our people are at the heart of this journey and together, we are driving a customer-centric transformation that values bold thinking, innovation, and the courage to challenge what’s possible. This is more than a strategic shift. It’s a chance for driven professionals to grow, learn, and make a real difference. If you are interested in exploring the possibilities We Want to Talk to You! The Difference You Make: The Risk Model Validation Associate conducts independent validations of complex risk models against established standards, developing benchmarks, challenger analyses, and replication testing, while leading special projects and programs as needed.
Position Summary Review and assess overall model health within established governance frameworks, proactively identifying risks, issues, and control gaps. Perform independent, end-to-end validations of a broad range of statistical, machine learning, and GenAI models in accordance with internal standards and regulatory expectations. Develop and execute effective benchmarks, challenger models, and replication analyses to evaluate model soundness and performance. Evaluate model assumptions, limitations, and weaknesses, and clearly document findings, conclusions, and remediation recommendations. Partner closely with model owners and developers to understand model design, intended use, and underlying business context. Assess ongoing model performance and monitoring results to ensure continued fitness for purpose. Ensure full adherence to company model risk management policies, procedures, and documentation requirements.

Requirements

• Bachelor's Degree or equivalent work experience – Required.
• Master’s Degree or higher in Statistics, Mathematics, Engineering, Computer Science, Economics, or a related field – Preferred.
• 5+ years of experience working with statistical models or building GenAI/ML solutions, including rigorous testing, validation, and documentation – Required.
• Experience in the financial services industry, including risk modeling and regulatory compliance (e.g., SR 11-7) – Preferred.
• Programming proficiency in Python, with experience using Git; SQL experience - Required.
• Machine learning model knowledge, including model development, validation, and performance assessment techniques.
• Generative AI experience, including evaluation of LLM architectures, design decisions, implementation approaches, and associated risks such as hallucinations, toxicity, and bias.
• Hands-on experience with Retrieval-Augmented Generation (RAG) systems, including document ingestion, chunking strategies, embeddings, retrieval methods, orchestration frameworks (e.g., LangChain, LlamaIndex), and response grounding.
• Hands-on experience with AWS-native services such as Bedrock, Lambda, API Gateway, S3, IAM, CloudWatch, and MLOps/LLMOps capabilities.
• Strong analytical and critical thinking skills with the ability to challenge model assumptions and conclusions.
• Effective written and verbal communication skills, particularly in documenting and explaining complex technical topics.
• Collaborative mindset with the ability to work effectively with technical and business stakeholders.
• High attention to detail and commitment to quality, accuracy, and governance standards.

Nice To Haves

• Established work history or equivalent demonstrated through a combination of work experience, training, military service, or education.
• Experience in Microsoft Office products.

Responsibilities

• Review and assess overall model health within established governance frameworks, proactively identifying risks, issues, and control gaps.
• Perform independent, end-to-end validations of a broad range of statistical, machine learning, and GenAI models in accordance with internal standards and regulatory expectations.
• Develop and execute effective benchmarks, challenger models, and replication analyses to evaluate model soundness and performance.
• Evaluate model assumptions, limitations, and weaknesses, and clearly document findings, conclusions, and remediation recommendations.
• Partner closely with model owners and developers to understand model design, intended use, and underlying business context.
• Assess ongoing model performance and monitoring results to ensure continued fitness for purpose.
• Ensure full adherence to company model risk management policies, procedures, and documentation requirements.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees