Risk Modeling & Data Governance Associate

Santander • Boston, MA

21h

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 Modeling & Data Governance Associate serves as a subject matter expert in designing and implementing end-to-end data governance frameworks for key reporting and business processes, partnering with business leaders to ensure data quality and timely delivery. The role also performs independent challenge and validation of complex risk models in accordance with Data Management and Model Validation standards, including benchmark development, challenger analysis, and replication testing, while supporting special projects as needed.

Requirements

• Bachelor’s Degree or equivalent work experience – Required.
• 7+ years of experience working with statistical models, developing, and/or validating machine learning or Generative AI solutions, including rigorous testing and documentation – Required.
• Quantitative or analytical professional background in Finance, Economics, Statistics, Mathematics, Engineering, or a related discipline – Required.
• Experience in the financial services industry with exposure to data management, risk modeling, and regulatory compliance (e.g., BCBS 239, SR 11-7) – Required.
• Knowledge of machine learning models, including development, validation, performance testing, and monitoring techniques.
• Generative AI expertise, including evaluation of LLM architectures, design decisions, implementation approaches, and associated risks such as hallucinations, bias, and toxicity.
• Hands-on experience with Retrieval-Augmented Generation (RAG) systems, including document ingestion, chunking strategies, embeddings, retrieval techniques, orchestration frameworks, and response grounding.
• Hands-on experience with AWS-native services such as Bedrock, Lambda, API Gateway, S3, IAM, CloudWatch, and MLOps or LLMOps capabilities.
• Strong Python proficiency for data manipulation, validation, automation of controls, and development of reproducible, well-documented scripts using version control.
• SQL proficiency, including data extraction, joins, aggregation, reconciliation, and validation across multiple data sources.
• Experience using Power BI to develop dashboards that visualize data quality metrics, trends, and validation outcomes.
• Demonstrated ability to operate effectively within a controlled, regulated environment with strong documentation standards and a risk-and-controls mindset.
• Proven ability to communicate complex technical, data, and model risk concepts clearly to non-technical governance and risk stakeholders.
• Strong analytical and critical thinking skills with the ability to independently challenge model assumptions and conclusions.
• Effective written and verbal communication skills, particularly in documenting and explaining complex technical and governance topics.
• Collaborative mindset with the ability to partner effectively with technical teams and business stakeholders.
• High attention to detail and a strong commitment to quality, accuracy, and governance standards.

Nice To Haves

• Master’s Degree or higher in Statistics, Mathematics, Engineering, Computer Science, Economics, or a related field – Preferred.
• Prior consulting, advisory, or second-line oversight experience within data governance, model validation, or risk analytics environments – Preferred.
• Established work history or equivalent demonstrated through a combination of work experience, training, military service, or education.
• Experience in Microsoft Office products.

Responsibilities

• Identify, document, and assess model data inputs to evaluate completeness, consistency, lineage, and the effectiveness of controls supporting model use.
• Define, document, and execute data validation requirements for model validation activities, including scope, control design, testing approaches, and evidentiary standards.
• Perform independent, end-to-end validation of statistical, machine learning, and Generative AI models in alignment with internal governance frameworks and regulatory expectations.
• Develop and execute benchmarks, challenger models, and replication analyses to assess model soundness, stability, and performance.
• Evaluate model assumptions, limitations, monitoring results, and performance metrics, documenting findings, conclusions, and remediation recommendations.
• Assess overall model health and compliance with data and model risk management policies, proactively identifying risks, issues, and control gaps.
• Partner with model owners, developers, and business stakeholders to understand model design, intended use, and business context while providing effective challenge.
• Support ongoing governance initiatives and special projects related to data quality, model risk, and regulatory readiness as needed.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level