Quantitative Analytics & Model Analyst Senior - Credit Loss Forecasting (CLF)

Position Overview At PNC, our people are our greatest differentiator and competitive advantage in the markets we serve. We are all united in delivering the best experience for our customers. We work together each day to foster an inclusive workplace culture where all of our employees feel respected, valued and have an opportunity to contribute to the company’s success. As a Quantitative Analytics & Model Analyst Senior within PNC's Credit Loss Forecasting (CLF) organization, you will be based in Pittsburgh, PA; Cleveland, OH; Tyson's Corner, VA; Washington, DC; East Brunswick, NJ; or Charlotte, NC. Job Summary: The Quantitative Analytics & Model Analyst Senior is responsible for contributing to the design, development, implementation, and governance of quantitative analytics, model implementation, and AI-enabled automation supporting CECL, CCAR, Model Monitoring, and other strategic risk initiatives. This role requires strong Python software-development skills, solid quantitative and data-engineering capabilities, and experience delivering reliable analytical solutions in enterprise environments. The successful candidate will contribute hands-on across the solution lifecycle, translate business and risk requirements into scalable capabilities, apply sound controls and testing standards, and partner with stakeholders across modeling, technology, validation, risk, and business teams. The role will also help evaluate and introduce machine learning and generative AI capabilities where they provide clear business value and can be implemented with appropriate security, explainability, governance, and human oversight. Key Responsibilities: • Contribute to the design, development, implementation, and enhancement of quantitative analytics, model-support, and AI-enabled automation solutions for CECL, CCAR, Model Monitoring, and other regulatory or business-critical initiatives, providing hands-on support from requirements and design through controlled deployment and production support. • Develop and review modular, testable, production-ready Python and PySpark code, data pipelines, controls, and workflow automation, applying established standards for code quality, version control, testing, documentation, security, and change management. • Support model onboarding, implementation, monitoring, and remediation, including developing and executing unit, integration, regression, reconciliation, and performance testing to confirm implementation accuracy and operational readiness. • Diagnose and resolve issues involving model execution, data quality, workflow orchestration, configuration, and analytical outputs; analyze large, complex datasets to assess model performance and investigate discrepancies, escalating complex issues as appropriate. • Evaluate and apply generative AI, and LLM-based capabilities where appropriate to improve analytics, model operations, monitoring, and documentation, with appropriate testing, security, human oversight, and governance controls. • Support model and analytical governance, including risk assessments, validation support, audit and regulatory responses, and issue remediation, and prepare clear, supportable technical and governance documentation. • Partner with model owners, development, validation, technology, and business teams; participate in design and code reviews; and communicate findings and recommendations clearly to technical and non-technical stakeholders. Required Qualifications: • Bachelor's or Master's degree in Quantitative Finance, Statistics, Mathematics, Computer Science, Data Science, Engineering, Operations Research, or a related quantitative field; advanced degree preferred. • Solid experience in quantitative analytics, model implementation, model monitoring, or AI/ML solutions in enterprise environments, including onboarding, testing, output validation, production deployment, and issue remediation. • Strong hands-on Python programming experience building modular, testable, production-ready solutions, including automated workflows, data pipelines, controls, and reconciliation processes using large, complex datasets. • Solid knowledge of statistical modeling, machine learning, and quantitative methods. • Demonstrated ability to diagnose and resolve issues involving code, data, workflows, and analytical outputs, applying standards for testing, documentation, version control, and change management. • Understanding of model risk, data privacy, information security, responsible AI, and governance requirements. • Strong communication and collaboration skills, with the ability to manage multiple priorities and explain technical concepts to technical and non-technical audiences. Preferred Qualifications: • Hands-on experience with PySpark, SQL, Git/Bitbucket, APIs, Parquet and CSV data, Slurm or comparable distributed job scheduling, and enterprise model-execution platforms and workflow orchestration. • Experience with quantitative models in financial services, particularly credit risk, CECL, CCAR, stress testing, or model governance, along with knowledge of banking products and regulatory expectations in highly regulated environments. • Experience applying AI/ML or generative AI/LLM-based solutions to analytical or model workflows, including agentic workflows, retrieval-augmented generation, vector databases, evaluation, and guardrails. • Experience participating in design and code reviews and mentoring junior team members. PNC is an in-office company that fosters a supportive culture where employees can thrive and achieve balance. We encourage candidates to connect with their recruiter and hiring manager to understand workplace expectations and ensure the role aligns with their goals.   PNC will not provide sponsorship for employment visas or participate in STEM OPT for this position. Job Description Independently performs advanced quantitative analyses and model development to drive decision-making by running quantitative strategies. Makes