Senior Quant, Artificial Intelligence/Machine Learning

At U.S. Bank, we’re on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed. We believe it takes all of us to bring our shared ambition to life, and each person is unique in their potential. A career with U.S. Bank gives you a wide, ever-growing range of opportunities to discover what makes you thrive at every stage of your career. Try new things, learn new skills and discover what you excel at—all from Day One. ## **Job Description** We’re looking for a sharp individual contributor who’s adept at advanced AI/ML algorithms and their applications in financial institutions to join our AI/ML Validation Center of Excellence in Model Risk Management. The team plays a critical role in providing oversight to U.S. Bank’s Artificial Intelligence Models across various business areas, such as Marketing, Fraud, Credit Risk and Bank Operations. As a Senior Quant you will develop benchmark AI/ML models, provide validation expertise and consulting services related to AI/ML model development and model review, including best practices on algorithm development and selection, performance evaluation, implementation, monitoring, model risk mitigation and remediation. In addition, you will be responsible for conducting R&D for various AI/ML methodologies and their potential applications and use cases. Deliverables include reviewing model development documentation, independently testing of advanced AI/ML and Generative AI & Agentic AI models, developing technical guidance documents, training curriculum and white paper, and communicating model requirements and validation outcome to stakeholders within the Bank. **Basic Qualifications** - Bachelor’s degree in a quantitative field, and 10 or more years of relevant experience OR - MA/MS in a quantitative field, and six or more years of related experience OR - PhD in a quantitative field, and five or more years of related experience **Preferred Skills/Experience** - Strong statistical modeling or computer science background and hands on model development or validation skills - Strong programming skills using Python packages such as Numpy, Pandas, and scikit-learn. - Considerable knowledge of various machine learning algorithms and their applications, including Random Forest, GBM, XGBoost, deep learning, NLP, computer vision, and LLM. - Hands-on experience designing, developing, and deploying advanced deep learning architectures, including MLPs, RNNs, CNNs, and other state-of-the-art neural network frameworks for a wide range of AI and machine learning applications. - Deep expertise in advanced Agentic AI architectures and orchestration patterns, including function calling, MCP, SKILLs, A2A, context engineering, harness engineering, loop engineering, and other emerging frameworks for building scalable, multi-agent AI systems. - Strong expertise in building, deploying, and evaluating GenAI and Agentic AI solutions, including RAG, multi-agent systems, tool-augmented workflows, and advanced evaluation techniques such as adversarial testing, semantic similarity analysis, retrieval assessment, and LLM-as-a-Judge methodologies. - Hands-on experience with modern AI development, deployment, and evaluation ecosystems, including PyTorch, TensorFlow/Keras, Hugging Face Transformers, LangChain, LangGraph, OpenAI Agent SDK, AI-assisted development platforms such as Claude Code and GitHub Copilot, and LLM evaluation frameworks including DeepEval, LlamaIndex, Ragas, and other comparable tools. - Familiarity with cloud-based AI platforms and services, including AWS Bedrock, Azure AI, Microsoft Copilot, Google Vertex AI, vector databases, and model serving/inference platforms. - Research experience and publications on AI or Gen AI are preferred - Experience in financial industry is preferred but not required - Advanced understanding of Model Risk Management and OCC SR 26-2 is a plus - Demonstrated…