Please note: This position is M-F during standard business hours with a hybrid work model (4 days in-office, 1 day working from home). It is only available in the areas listed. Candidate must reside or be willing to relocate on their own to one of the listed areas. Applicants must be currently authorized to work in the United States on a full-time basis without employer sponsorship.

What you will do

• Validate AI and machine learning models by independently assessing intended use, design choices, data, implementation, controls, performance, and limitations.
• Build reproducible testing frameworks, analyses, and prototypes to evaluate model quality, robustness, safety, and reliability.
• Evaluate generative AI and retrieval-augmented generation applications, including prompt behavior, retrieval quality, groundedness, hallucination risk, response consistency, and guardrail effectiveness.
• The candidate will apply statistical and machine learning methods to benchmark models, analyze errors, compare alternatives, and assess results.
• Review source code, notebooks, technical documentation, model inventories, performance monitoring, and production-control evidence.
• Partner with model developers, product teams, data scientists, engineers, risk partners, and external consultants to understand systems and challenge assumptions constructively.
• Document findings in clear validation reports, technical appendices, issue statements, and executive-ready presentations.
• Evaluate ongoing performance monitoring and participate in periodic reviews of models after implementation.
• Contribute to reusable validation frameworks, testing utilities, and effective-challenge methods as AI capabilities and industry practices evolve.

What you will learn

• How to design production AI systems, testing, governance, and monitoring in a large financial institution.
• How to translate open-ended model risk questions into structured experiments and defensible conclusions.
• How to communicate technical findings to audiences ranging from ML engineers to senior management.
• How responsible AI, model risk management, software controls, and regulatory expectations come together in practice.

Required Qualifications

• Master's degree, or bachelor's degree with strong relevant project or research experience, in computer science, data science, statistics, applied mathematics, engineering, economics, quantitative finance, or a related field.
• 0-2 years of professional experience. Relevant internships, research, capstone projects, open-source contributions, or substantial independent projects are welcome.
• Proficiency in Python and experience using common data science or machine learning libraries.
• Working knowledge of core machine learning concepts, experimental design, model evaluation, and statistical reasoning.
• Ability to read technical documentation and code, investigate unfamiliar systems, and communicate conclusions clearly.
• Curiosity, sound judgment, attention to detail, and a willingness to challenge assumptions with evidence.

Preferred Qualifications

• Hands-on experience with large language models, generative AI, natural language processing, embeddings, vector search, or retrieval-augmented generation.
• Experience with evaluation frameworks, prompt testing, red teaming, model monitoring, or responsible AI techniques.
• Familiarity with SQL, Git, containers, cloud platforms, or modern ML development workflows.
• Understanding software testing, data quality, reproducibility, or model deployment concepts.
• Experience presenting technical work through research papers, project reports, demos, or presentations.
• Interest in financial services, model risk management, AI governance, or building trustworthy AI systems.

How you will succeed

You do not need to arrive as an expert in financial regulation or model validation. Successful candidates bring strong technical fundamentals, intellectual curiosity, disciplined problem solving, and the ability to explain complex work clearly. We value thoughtful individuals who enjoy learning quickly, testing ideas, finding weaknesses before they become problems, and improving evaluation of AI systems.

Role details

• Career level: Senior Specialist, Risk Analytics/Modeling
• Team: Model Risk Oversight, Artificial Intelligence Validation
• Role type: Individual contributor
• Primary focus: AI/ML and generative AI model validation, effective challenge, and ongoing model oversight